text:
Keystone XL: Where Things Stand
It's been one month since the climate movement won a significant delay on the Keystone XL pipeline. Since then, the oil industry and their political and media backers have gotten increasingly desperate:
- Oil companies tried to ram a vote on Keystone through the U.S. Senate. Last week, that effort collapsed in disarray and finger-pointing among the fossil fuel industry's biggest political boosters.
- TransCanada, the Canadian company behind Keystone, even resorted to threats to sue the U.S. government under NAFTA. Early this month, they were forced to disavow that outrageous tactic.
- Pro-Keystone commentators are truly grasping at straws, including claiming that President Obama is delaying a decision to pave the way for a government takeover of the energy sector,1 and arguing that it's the poor, under-resourced oil industry—and not the environmental movement—that's the real underdog in the fight over the pipeline.2
These bottom-of-the-barrel tactics signal that fossil fuel corporations will do anything to avoid facing up to the view that one prominent analyst voiced this month: "We have been of the opinion for nearly a year now that Keystone XL is dead."3This delay means another year that tar sands oil stays in the ground, instead of flowing through the pipeline. This delay is another nail in the coffin of this disastrous project. And you—the incredible grassroots tide of resolve and determination—are the ones who made this happen.
Keystone would have been just another routine dirty energy infrastructure project if not for public pressure—like the unprecedented 2.5 million public comments submitted into the approvals process. People all along the pipeline route, from Alberta to the Gulf Coast—especially Indigenous communities and farming communities—mobilized against the project.
Another key factor has been the threat of massive civil disobedience if President Obama approves the pipeline—one veteran environmental campaigner called it the "sharpest arrow in the quiver" of the Keystone opposition movement. Almost 100,000 people signed Keystone XL Pledge of Resistance, committing to take direct action if the administration lights the fuse of the continent's biggest carbon bomb.
So while we're proud that the movement won a major delay, the struggle is far from over. Here at Rainforest Action Network, we're staying vigilant on Keystone. We're continuing to hone the cutting edge of the climate movement by training committed activists. And we're taking fast, strategic action to block dirty energy deals.
Thank you for all you've done.
1. "Obama Blocks Keystone To Start Energy Takeover," Investor's Business Daily, May 13, 2014 (http://news.investors.com/ibd-editorials-perspective/051314-700702-obama-wants-energy-markets-fossil-fuels-under-government-heel.htm)
2. "Mainstream media don't know Big Green has deeper pockets than Big Oil," Washington Examiner, May 13, 2014 (http://washingtonexaminer.com/mainstream-media-dont-know-big-green-has-deeper-pockets-than-big-oil/article/2548405)
3. "The Keystone Pipeline is Quickly Becoming Obsolete," Business Insider, May 7, 2014 (http://www.businessinsider.com/the-keystone-pipeline-is-quickly-becoming-obsolete-2014-5)
Extreme Coal - No Longer Business as Usual
For the first time since we began publishing coal finance report cards five years ago, we have an encouraging trend to report: Major banks have begun making noise about the growing financial risk associated with climate change—and specifically associated with coal, the top global contributor to carbon pollution.
On top of that, major banks have begun to cut ties with the biggest mountaintop removal (MTR) coal companies. This progress has exposed a growing gap between banks that are still sinking billions into coal, and those that are cutting ties with the worst-of-the-worst in the coal industry.
Today, RAN, the Sierra Club, and BankTrack released our 2014 Coal Finance Report Card, "Extreme Investments, Extreme Consequences," which grades U.S. banks on their performance and policies related to coal-fired power and mountaintop removal coal mining. We also uncovered the top financiers of contentious coal export schemes like those in the Pacific Northwest and coal trains that transport dusty coal across the United States.
All told, banks sank over $31 billion into the worst companies in the coal industry last year, with $6.5 billion coming from Citigroup, the top funder of coal-fired power. However, JPMorgan Chase and Wells Fargo began to phase out financing for MTR, earning our first ever "B" grades, and marking a positive trend away from the extreme mining practice.
Meanwhile, UK-based Barclays increased its exposure to MTR, financing $550 million for mountaintop removal coal companies last year, more than any other bank.
Environmental damage from mining, transporting, and burning coal—including health hazards like air pollution and water contamination from spills—doesn't just harm communities and the environment, it costs banks money. In the report card, we highlight examples of this in case studies about the rising cost of clean-up for water contamination at mine sites, increases in coal company bankruptcies, and money-losing coal-fired power plants.
Download the 2014 Coal Finance Report Card.
The report comes on the heels of analyst publications from Goldman Sachs, HSBC and Citigroup last year, each of which challenged the case for continued investment in the coal industry. These and other banks have acknowledged that power plant regulations, a potential price on carbon, and competition from renewable energy sources could "strand" assets such as coal mining, transport, and power generation facilities. With billions of dollars in loans on the line, it's not a question of if climate risk will translate into financial risk, but when.
Ironically, these very same banks maintain deep financial ties to the riskiest and most environmentally destructive companies in the U.S. coal industry. As credit ratings for some coal mining companies sank farther below investment grade last year, banks continued to place bets on risky loans to the sector.
The report card warns banks that before the carbon bubble bursts onto their balance sheets, it will irreversibly destabilize the climate. So while we are happy to report that a few banks took the first steps to cut off financing to the worst-of-the-worst of the coal industry, the banking industry as a whole must now cut its losses and forge a path away from coal, before it's too late for both them and us.
TAKE ACTION: Tell Barclays, the number one banker of mountaintop removal, to end its support of destroying mountains and poisoning communities for coal.
PepsiCo Says No More Land Grabbing; Now Needs To Cut Conflict Palm Oil and Deforestation
Today, food and beverage giant PepsiCo declared that it will no longer accept land grabbing in its global supply chains. Land grabbing occurs when Indigenous Peoples or local communities are kicked off their land so corporations can make profits from growing palm oil, sugar, and other crops.
This announcement comes after significant consumer pressure from Oxfam's Behind the Brand Campaign and PepsiCo investors who called on the company to adopt a zero-tolerance policy for land grabbing.
The adoption of its new Land Policy is a positive step forward for PepsiCo, but we know all too well that actions are stronger than words. PepsiCo must take real action to deal with its land grabbing problem. Given that land grabbing is also a symptom of PepsiCo's deforestation and Conflict Palm Oil problems, it must now make the next bold move and implement a responsible palm oil sourcing and no deforestation policy.
PepsiCo is a huge company, operating in over 200 countries and earning $65.6 billion in revenue each year. Its well known brands, including Pepsi, Doritos, Ruffles, Cheetos and Quaker, are found in homes around the world. RAN has exposed the dangers of Conflict Palm Oil and the fact that it ends up in chips, cookies and granola bars made by PepsiCo. PepsiCo sources its palm oil from companies like Cargill, Wilmar and AAK in Indonesia, Malaysia and Mexico.
Despite the growing concern over Conflict Palm Oil, PepsiCo has not adopted a responsible palm oil policy to remove deforestation and social conflict from its global supply chain. Instead of taking responsibility for its supply chain, the company relies solely on the Roundtable on Sustainable Palm Oil (RSPO). The RSPO continues to certify companies that are destroying rainforests and peatlands and causing high greenhouse gas emissions. It also has a poor track record of enforcing its human and labor rights standards, and resolving disputes between certified companies and local communities over land grabbing.
PepsiCo cannot rely on the RSPO.
To address these problems fully, PepsiCo must join other leading consumer companies and adopt responsible palm oil sourcing and no deforestation policies and cut Conflict Palm Oil from its products.
You can help pressure PepsiCo to tackle its deforestation and Conflict Palm Oil problems next.
On May 7th, PepsiCo will need to face up to its shareholders who will be casting their vote on a deforestation resolution at its annual general meeting. With your help, we'll convince PepsiCo to do the right thing for the forests and the people that depend on them for their survival. Add your voice here.
Banner photo via C.J. Chanco Inset photo via Oxfam
7 Facts You Didn't Know About What You're Eating
Sometimes the problem of climate change and environmental destruction seems so large that it feels insurmountable. We've changed our light bulbs to those weird looking things, and we signed up for the Pledge of Resistance. What more can we do? The answer is as close as your kitchen. A United Nations Environment Program report in 2006 stated that "rearing cattle produces more greenhouse gases than driving cars." So what's the magic light bulb fix for that? Switching to a plant-based diet would be a good start in helping to alleviate such a burden. Where do you start? How about joining Rainforest Action Network on March 20 by taking the Farm Animal Rights Movement's pledge to eat only plant-based foods. And in the meantime, arm yourself with these facts that show how even a small change can make a big difference.
1. About 70 billion farm animals are killed every year for food—that's over 100,000 animals every second.
Besides the ethical implications, those animals all need places to live, food to eat, and water to drink, and that's no easy, or clean, feat.
2. It takes 1500 gallons of water to produce one pound of meat.
As droughts become more prevalent, keep in mind that not eating one pound of meat saves as much water as eliminating four months of 5-minute showers.
3. Animals raised for food in the US produce 31,000 pounds of manure a second.
When you gotta go, you gotta go. But when you're going that much, where does all of it end up?
4. That manure has to go somewhere.
The number 1 dairy producer in the US, California, identified agriculture, including cows, as the major source of nitrate pollution in more than 100,000 square miles of polluted groundwater (61% of the entire state).
5. Pig, chicken and cow waste has polluted 35,000 miles of rivers in 22 states and contaminated groundwater in 17 states.
That's over 8 times the length of the Nile, the longest river in the world.
6. All is not lost. The United Nations Environment Program has the solution:
"A substantial reduction of impacts would only be possible with a substantial worldwide diet change, away from animal products."
7. By switching to a plant-based diet, you're helping yourself, the Earth, and you're creating 70 billion smiles.
That's something we can all feel good about. Take the pledge today.
Climate Culprits Exposed In New Scorecard
Stand With Orangutans On World Wildlife Day
VIDEO: Thousands Take Action Saying #NOKXL
//www.youtube.com/embed/EyJ70Dqz5cQMore than 78,000 have already signed the Keystone XL Pledge of Resistance, committing to risk arrest, if necessary, to stop Keystone XL and send a message to the President that we must move forward on climate. We must defeat Keystone XL. Join us and take the pledge: www.nokxl.org/pledge
New Keystone XL Review Acknowledges Significant Climate Impact
Investor Summit: Bank of America puts our Climate at Risk
This post is by Yann Louvel, BankTrack Climate and Energy Campaign Coordinator.
Earlier this month, Bank of America participated in the 2014 Investor Summit on Climate Risk as the "convening sponsor" of the event. While there was a lot of talk about the urgency of the problem of tackling climate change, there were a few things the bank didn't talk about. For starters, their role in financing the coal industry.
The 2014 Investor Summit on Climate Risk took place at the United Nations in New York on January 14th. Among the speakers was Lisa Carnoy, Head of Global Capital Markets for Bank of America Merrill Lynch, whose bio tells us that she "leads 700 Capital Markets professionals across Equity Capital Markets, Debt Capital Markets, Leveraged Finance and Origination of Corporate Derivatives and FX across the Americas, Europe, Asia and the Emerging Markets."
If the bank's finance for fossil fuels wasn't mentioned at all, what do you think Carnoy did talk about in her speech? You may be pleased to hear that she conveyed the great imperative felt by the banking sector on climate change. Bank of America's approach to the issue would be "like a hockey team: we're fierce, we're fast, and we feel the urgency." What's more, the banking sector as a whole was coming together to "put aside its natural competitiveness," because "this is incredibly important, the time is now, and we need to work together."
Great! So how had this fierce, fast hockey team come together to tackle climate change? The one initiative Carnoy presented was the Green Bonds Principles, a set of voluntary guidelines for how banks can develop and issue bonds to support green industries, which she implored investors to get behind.
While we support any efforts to scale up finance for genuine alternatives to fossil fuels, Bank of America's backing for green bonds is dwarfed by the activities that Carnoy did not talk about. These are the activities that we have been exploring in our most recent reports: its finance for the coal sector. Bank of America was ranked the number three "climate killer bank" worldwide in the BankTrack network's 2011 Bankrolling Climate Change report, which covered investments in 70 of the largest coal companies between 2005 and 2011. And in BankTrack's more recent Banking on Coal report, Bank of America again ranked world number three, this time in its finance for coal mining, based on an analysis of finance for 70 coal mining companies worldwide.
Among the deals Carnoy did not talk about are some (not remotely green) bonds issued by her Capital Markets team over the last two years, which helped to raise over $1 billion for Alpha Natural Resources and Arch Coal. These companies are pure-play US coal miners, and are being targeted by campaigners for their involvement in destructive mountaintop removal coal mining in Appalachia. These bonds are toxic for our climate as well as for the investors who buy them, spreading climate risk through the financial markets in the form of potential future "stranded assets."
While Bank of America is asking investors to back the Green Bonds Principles, it is investors who should be asking Bank of America to stop feeding them with these financially risky climate killer bonds. And now that Bank of America feels the "urgency" and "imperative" of tackling climate change, it would do well to stop financing climate change through issuing bonds for coal mining.
It is time for banks to come together, put aside their natural competitiveness, and agree to stop financing coal. Because this is incredibly important. The time is now.
West Virginia Chemical Leak Poisons 300,000 Peoples' Water Supply
Since last Thursday's toxic spill, when a coal-processing chemical spilled into West Virginia's Elk River, roughly 300,000 people have lost access to tap water. Our friends at groups like OVEC, Keepers of the Mountain, Coal River Mountain Watch and Aurora Lights are volunteering and working long days to drive clean water supplies to desparate and remote communities throughout the nine affected counties in West Virginia. A few dollars goes a long way to help. Click any of those links to donate. I just donated and want to urge you to consider making a donation to water relief efforts too. Yet again Appalachian communities are being disenfranchised. This industrial disaster is not getting much play in the national media, despite being just a few hours from the nation's capital. Meanwhile, West Virginia's Governor keeps insisting that this disaster has nothing to do with the coal industry. To be clear, the chemical that spilled (4-methylcyclohexane methanol) is a chemical that is produced for use producing coal (the "cheapest" form of energy in this country) and the reason that a relatively small spill was able to impact so many people's drinking water (16% of the state) is that decades of contamination from coal mining and processing means that many rural communities can no longer rely on well water, and instead have to connect the municipal water systems. Then the privatization of public infrastructure means that the business has been aggressively consolidated into even larger distribution networks. Combine all that with the regulatory joke that is West Virginia's "Department of Environmental Protection" (the site's last inspection was in 1991), and you have a disaster on your hands. Ken Ward (IMO the smartest journalist covering these issues) wrote this morning:
Plenty of West Virginia communities have watched their drinking water supplies be either polluted or dried up because of coal (see here, here and here). Me and my neighbors are getting a taste right now of what some coalfield residents live with all the time.
If you want to help spread awareness of this tragedy and the urgent need for support of the affected West Virginians, please share this post on Facebook.


text:
Social Network Analysis and Mining (SNAM) techniques have drawn significant attention in the recent years due to the popularity of online social media. With the advance of Web 2.0 and SNAM techniques, tools for aggregating, sharing, investigating, and visualizing social network data have been widely explored and developed. SNAM is effective in supporting intelligence and law enforcement force to identify suspects and extract communication patterns of terrorists or criminals. In our previous work, we have shown how social network analysis and visualization techniques are useful in discovering patterns of terrorist social networks. Attribute to the advance of SNAM techniques, relationships among social actors can be visualized through network structures explicitly and implicit patterns can be discovered automatically. Despite the advance of SNAM, the utility of a social network is highly affected by its d completeness. Missing edges or nodes in a social network will reduce the utility of the network. For example, SNAM techniques may not be able to detect groups of social actors if some of the relationships among these social actors are not available. Similarly, SNAM techniques may overestimate the distance between two social actors if some intermediate nodes or edges are missing. Unfortunately, it is common that an organization only have a partial social network due to its limited information sources. In public safety domain, each law enforcement unit has its own criminal social network constructed by the data available from the criminal intelligence and crime database but this network is only a part of the global criminal social network, which can be obtained by integrating criminal social networks from all law enforcement units. However, due to the privacy policy, law enforcement units are not allowed to share the sensitive information of their social network data. A naive and yet practical approach is anonymizing the social network data before publishing or sharing it. However, a modest privacy gains may reduce a substantial SNAM utility. It is a challenge to make a balance between privacy and utility in social network data sharing and integration. In order to share useful information among different organizations without violating the privacy policies and preserving sensitive information, we propose a generalization and probabilistic approach of social network integration in this paper. Particularly, we propose generalizing social networks to preserve privacy and integrating the probabilistic models of the shared information for SNAM. To preserve the identity of sensitive nodes in social network, a simple approach in the literature is removing all node identities. However, it only allows us to investigate of the structural properties of such anonymized social network, but the integration of multiple anonymized social networks will be impossible. To make a balance between privacy and utility, we introduce a social network integration framework which consists of three major steps: (i) constructing generalized sub-graph, (ii) creating generalized information for sharing, and (iii) social networks integration and analysis. We also propose two sub-graph generalization methods namely, edge betweenness based (EBB) and K-nearest neighbor (KNN). We evaluated the effectiveness of these algorithms on the Global Salafi Jihad terrorist social network.
Social Network Analysis and Mining (SNAM) techniques have drawn significant attention in the recent years due to the popularity of online social media. With the advance of Web 2.0 and SNAM techniques, tools for aggregating, sharing, investigating, and visualizing social network data have been widely explored and developed. SNAM is effective in supporting intelligence and law enforcement force to identify suspects and extract communication patterns of terrorists or criminals. In our previous work [1-3], we have shown how social network analysis and visualization techniques are useful in discovering patterns of terrorist social networks. Attribute to the advance of SNAM techniques, relationships among social actors can be visualized through network structures explicitly and implicit patterns can be discovered automatically.
Despite the advance of SNAM, the utility of a social network is highly affected by its d completeness. Missing edges or nodes in a social network will reduce the utility of the network. For example, SNAM techniques may not be able to detect groups of social actors if some of the relationships among these social actors are not available. Similarly, SNAM techniques may overestimate the distance between two social actors if some intermediate nodes or edges are missing. Unfortunately, it is common that an organization only have a partial social network due to its limited information sources. In public safety domain, each law enforcement unit has its own criminal social network constructed by the data available from the criminal intelligence and crime database but this network is only a part of the global criminal social network, which can be obtained by integrating criminal social networks from all law enforcement units. However, due to the privacy policy, law enforcement units are not allowed to share the sensitive information of their social network data. A naïve and yet practical approach is anonymizing the social network data before publishing or sharing it. However, a modest privacy gains may reduce a substantial SNAM utility. It is a challenge to make a balance between privacy and utility in social network data sharing and integration.
In order to share useful information among different organizations without violating the privacy policies and preserving sensitive information, we propose a generalization and probabilistic approach of social network integration in this paper. Particularly, we propose generalizing social networks to preserve privacy and integrating the probabilistic models of the shared information for SNAM. To preserve the identity of sensitive nodes in social network, a simple approach in the literature is removing all node identities. However, it only allows us to investigate of the structural properties of such anonymized social network, but the integration of multiple anonymized social networks will be impossible. To make a balance between privacy and utility, we introduce a social network integration framework which consists of three major steps: (i) constructing generalized sub-graph, (ii) creating generalized information for sharing, and (iii) social networks integration and analysis. We also propose two sub-graph generalization methods namely, edge betweenness based (EBB) and K-nearest neighbor (KNN). We evaluated the effectiveness of these algorithms on the Global Salafi Jihad terrorist social network.
This paper is organized as follows. In the next section, we review the existing works about privacy preservation of social network. Previous techniques are classified based on their assumption of attack models the definition of sensitive information, and the privacy preservation techniques. In section 3, we introduce the researchd framework. Social network generalization and integration techniques are introduced in section 4. The experiment design, results and discussions are presented in section 5. We conclude our work and introduce future work in section 6.
Related work
Sensitive information of social network
Given a social network, the definition of sensitive information depends on the specific applications. In the literature, the social network sensitive information can be classified into node properties, neighborhood graphs, edge properties, and network properties in general.
Node properties
In a social network, identity of nodes can be an important type of sensitive property [4-7]. A node with sensitive identity means that its identity is private and should not be released. On the other hands, a node with insensitive identity means that the identity of this node can be released with no harm. Another type of sensitive property of a node can be its degree centrality [8-12]. Given a node, the degree centrality equals to the total number of edges connecting to this node, which is the number of friend in a social network. In a directed graph, edges can be further divided into in-links and out-links. Releasing the degree centrality of a given node, attacker can find out the number of nodes associated to this node which may further release its identity.
Neighborhood graphs
Node neighborhood graph is a concept highly related to degree centrality but with some differences [12]. Given a node and its neighbors, how these neighbors connect with each other can be unique. Publishing the neighborhood graph of a node may release the identify of this node.
Edge properties
Besides the properties of network nodes, Zheleva and Getoor also studied some sensitive properties related to network edges[13]. Two types of information of an edge can be potential sensitive information. One is the existence of an edge between two given nodes. The other is the label of a given edge which represents the type of relationship.
Network properties
Social network data has a set of important properties which can be considered as sensitive information in some cases, such as diameter, radius, betweenness, closeness, clustering coefficient etc.
Social network privacy attack model
To have a better protection against privacy attack, it is important to understand different types of privacy attack models. In this section, we introduce two categories of attack, active and passive attacks [11,14].
Active attacks
Backstorm et al. [14] introduced the active attack model. An adversary can actively select an arbitrary set of target actors, creates a small number of new actors with edges connecting to these targeted users, and then creates a pattern of links among the new actors. By planting new actors and connection patterns in the anonymized social network sophisticatedly, the adversary is able to identify the new actors as well as the targeted actors if the generated connection patterns are uniquely stand out in the anonymized network. Theoretically, the creation of nodes in an n-node network will begin compromising the privacy of the arbitrary targeted nodes. Backstorm et al., [14] further divided the active attacks into walk-based attack and cut-based attack. Both of them employed the strategy of inserting nodes into the target network and then link these nodes with the target nodes. The difference between them is the theoretical number of nodes used in the attack.
Passive attacks
Backstorm et al. [14] also investigated the passive attack model, where adversaries do not create any new nodes or edges. Backstorm et al. pointed out that attacker with certain knowledge can easily differentiate the target nodes or edges from the others due to their unique structural information. Most current studies focus their research on preventing passive attacks, which includes: (1) node passive attack [8-11], where adversaries are supposed to take advantage of node's degree centrality information to uncover node's identity; (2) edge passive attack [13-15], where adversaries are supposed to know the existence of certain edges, leading to the disclosure of sensitive information by tracking the identify of other edges or nodes via known edges; (3) sub-graph passive attack [9,11,12], where adversaries are supposed to make use of sub-graph information known in advanced to identify sensitive information of node, such as node identity; (4) graph metrics passive attack [16], where the adversaries have certain background knowledge of the graph metrics, for example hub fingerprint, closeness centrality or betweenness centrality. With the knowledge of these graph metrics, it's also possible that adversaries can uncover several sensitive information of the social network.
Privacy preservation models and algorithms
In the recent years, a number of approaches for preserving privacy of relational data have been studied extensively, which include k-anonymity [17,18], l-diversity[19], Personalized anonymity[20], and (α,k)-Anonymity[21]. One common objective of these algorithms is to ensure every node is indistinguishable to other (k-1) nodes after anonymization. Although these methods work well in relational table data, most of them cannot deal with social network data due to the complex structure of social network and various background and attack model employed by an adversary. In the recent years, a few research groups have investigated the privacy preservation of social network data. They preserve the data privacy mainly by three approaches: perturbation-based approach, generalization-based approach, and protocol-based approach. Different techniques correspond to different type of sensitive data as well as privacy requirement.
Perturbation-based technique
The perturbation-based technique perturbs a social network by adding, deleting or switching edges in a social network in order to increase the difficulty of identifying a node. Most of them are using greedy algorithm guided by an objective function to modify the social network step by step until the anonymized network satisfied some given conditions. Liu and Terzi proposed the K-degree Anonymous Algorithm to ensure that each network node is indistinguishable to other (K-1) nodes [10]. Starting from the original degree sequence d of input graph G, the algorithm constructs a new degree sequence which satisfies two conditions including: is k-anonymous and is minimized. Zhou and Pei proposed the K-neighborhood Algorithm to make sure that node identity cannot be re-identified by an adversary with a confidence larger than 1/k, even though the adversary has background knowledge of the neighborhood graph [12]. The whole process is divided into two phases. First, the algorithm extracts the neighborhoods of all nodes in the network. To facilitate the comparisons among neighborhoods of different nodes, the researchers proposed a neighborhood component coding technique to represent the neighborhoods in a concise way. In the second step, the algorithm greedily organizes nodes into groups and anonymizes the neighborhoods of nodes in the same group. The greedy algorithm is guided by an anonymization cost which is measured by the similarity between the neighborhoods of two nodes. Ying and Wu proposed the Spectrum Preserving Algorithm which preserves the privacy by randomly perturbing edges in the network [16]. The whole process can be divided into three steps: at first, the eigenvalues of the input graph is computed; and then based on some proved theorems, the boundaries of eigenvalues are given; finally the algorithm perturbs the graph by adding, deleting or switching edges of the graph. If the eigenvalues of perturbed graph is within the given boundaries, the perturbation is accepted and continued for next perturbation. The algorithm terminates until the precondition is satisfied.
Generalization-based technique
The generalization-based technique preserves a social network by grouping certain number of nodes or edges together and then only release the general information of the groups of nodes or edges. Nodes within a group cannot be differentiated because they all share exactly the same properties of the group. In most cases, a generalization-based technique divides nodes according to some predefined loss functions. Hay et al. proposed a node splitting-based technique to achieve k-anonymity of the social network [9,11]. Starting from a single partition of a social network, the algorithm keeps on splitting the selected partition into two sub-groups until all predefined criteria are satisfied. Similarly, Campan and Truta introduced a node clustering-based approach to satisfy the k-anonymity requirement and minimize the information loss [8]. In their algorithm, clusters are created one at a time. To form a new cluster, a node in V with the maximum degree but not yet be allocated to any cluster is selected as a seed for the new cluster. Then the algorithm puts nodes to this currently processed cluster until it reaches the desired cardinality k. At each step, the current cluster grows with one node. The selected node should not be assigned to any cluster yet but it should be able to minimize the growth of information loss of the current clusters. Zheleva and Getoor proposed an edge clustering-based technique to hide the sensitive information on edges [13]. Their technique is divided into two phases. In the first phase, the technique provides a clustering of the nodes into m equivalence class (C1,C2,…,CM) such that each node is indistinguishable in its quasi-identifying attributes from K-1 other nodes. In the second step, this work presents several techniques to protect sensitive information of the social network and then compare their performance, which includes partial-edge removal, cluster-edge anonymization, and cluster-edge anonymization with constraints. Cormode and Srivastava proposed the safe groupings technique for a bipartite Graph G = (V,W,E), where V and W correspond to two types of objects [15]. In their work, a safe grouping of a bipartite graph partitions nodes into groups such that two nodes of the same group of V have no common neighbors in W and vice versa. A greedy algorithm is proposed to find K safe groups of V and L safe groups of W. For each node u, the algorithm attempts to assign u to the first group with fewer than n nodes. If it makes the grouping unsafe, the algorithm will try the second available group and so forth. If there is no group that meets the requirements, a new group will be created to contain this node. After getting K safe groups of V, the algorithm move forward to find L safe groups of W following a similar same process.
Protocol-based technique
The protocol-based technique is using the encryption approach rather than anonymizing the social network data. Social network data is encrypted by following a protocol before sharing with other parties. The protocol ensures that other parties are only able to obtain the insensitive information for their applications but the sensitive information is preserved. Frikken and Golle proposed the pieces assembling approach for private social network analysis [22].
In this section, we provide a summary of the literature by comparing the privacy preservation techniques and the preserving data in social network as shown in Table 1. In general, some privacy preservation techniques are developed for preserving specific information but are not applied to other information. The choice of the privacy preservation techniques also depends on the application of social network analysis.
Table 1. Classification of privacy preservation techniques based on sensitive information
Research problem
The existing works focus on preserving privacy of social network data for data publishing so that the global network structure can be analyzed. However, it has not considered how to integrate social network data from different sources so that social network analysis and mining can be conducted on the integrated data and yet the privacy of the shared data can be preserved. Individual published social network data only capture parts of the complete social network. Unless we can integrate multiple social networks and conduct SNAM on the integrated social network, the utility of the anonymized data is still limited. Given multiple law enforcement units and each of them has a criminal social network which captures a partial picture of a complete criminal social network, the objective of this work is preserving the privacy of the shared data from each law enforcement unit and conduct SNAM tasks on the integrated data. In this section, we first define formally the research problem, introduce what sensitive information to preserve, what insensitive information to share and what SNAM task can be conducted. Then, we proposed a research framework to address the research problem.
Problem definition
Given a set of network ℊ = {G1, G2,…,Gn} in a distributed setting where each organization i owns its piece of Gi, assuming the complete network ) is unknown to each individual organization, the goal of this paper is to study how to anonymize each Gi into so that: 1) the sensitive identities of Gi can be protected; 2) can be shared with other organizations and the integrated anonymization graph can be used for SNAM task. Concretely, each network consists of both insensitive nodes and sensitive nodes. Node identities of those insensitive nodes are known to the public or the sharing parties while the node identities of those sensitive nodes are unknown to the public and needed to be protected. Our focus in this paper is to protect the node identities of those sensitive nodes. On the other hand, for SNAM purpose, some network properties, including topology, diameter and some other abstract features of the anonymized network, will be released and shared across organizations. Last but not least, it's important to note that some network features cannot be preserved in our method, such as neighborhood information. Therefore, not all SNAM tasks can be achieved in our integrated anonymized network. In this paper, we only study how to preserve the usefulness of the integrated anonymized network regarding to distance-related analysis, such as computing the closeness of each node. To summarize, although some existing works have studied how to anonymize network for data publishing, the research problem that we study here is different. We not only anonymize a given network to protect its' node identity but also focus on integrating anonymized networks to achieve better SNAM results
Framework of social network integration with privacy preservation
To further motivate our research framework, we assume organization P ( OP) has a social network GP and organization Q ( OQ) has another social network GQ, both GP and GQ are partial networks of a complete social network which is unknown to any organization. OP needs to conduct a Social Network Analysis and Mining (SNAM) but GP is incomplete due to its limited sources of information. As a result, it will be difficult or even impossible for OP to get accurate SNAM results. If there is no privacy concern between different organizations, one can integrate GP and GQ to generate an integrated G and obtain a better SNAM result. However, due to privacy concern, OQ cannot share GQ completely with OP, but only shares the insensitive information of GQ with OP according to the privacy policies. At the same time, OP does not need all data from OQ but only those that are critical for the SNAM tasks. For these reasons, to integrate social networks of different organizations without violating privacy policies, we only need to share information that is critical to the performance of SNAM and yet preserve the sensitive information.
Figure 1 demonstrates the general framework of social network integration for SNAM. In this framework, OQ employs sub-graph generalization techniques to create a generalized social network, GQ', from GQ without violating the privacy policy. The generalized social network only contains generalized information of GQ without releasing any sensitive information. For example, a generalized social network cannot release the exact identity of each nodes or exact shortest distance between any two nodes. On the other hand, generalized information can include diameter of a sub-graph, average number of adjacent nodes between two subgroups, degree of an insensitive node and other insensitive information. The generalized social network GQ' will then be integrated with GP to support a social network analysis and mining task. Given the generalized information from GQ, it is expected to achieve better performance on SNAM task than conducting the analysis and mining on GP alone. There are two important sub-tasks in our proposed framework which we will address in the following sections:
Figure 1 . General framework of social network integration for SNAM.
Task 1
Given a social network G with sensitive information, produce generalized social network G' and determine the generalized information which can be released.
Task 2
Integrate a generalized social network with the local social network, and then utilize shared generalized information to achieve better SNAM results.
In Table 2, we define a set of notations for the proposed social network integration techniques.
Table 2. Notations and definitions
Social network generalization
In task one, given a social network G with sensitive information, we employ clustering-based technique to produce a generalized social network G'. We suppose G = ( V, E), where V is a set of nodes, E is a set of edges and | V| = n, K of these nodes are insensitive nodes, and n-K of these nodes are sensitive nodes. We generate a generalized social network in two steps. In the first step, we decompose G into K sub-graphs Gi = (Vi,Ei), where V = Ui =1to KVi and each sub-graph contains one insensitive node. In the second step, each sub-graph will be transformed to a generalized node of the generalized graph G'. Furthermore, two generalized nodes will be connected in G' if and only if there is one or more edges connecting nodes from these two sub-graphs respectively.
In this section, we propose two graph partition algorithms, K-nearest neighbor ( KNN) method and Edge betweenness based (EBB) method, to generate a generalized social network G' for sharing purpose. Both KNN and EBB methods are developed by following one common principle that the identity of insensitive nodes can be published safely while the identity of sensitive nodes cannot, so that, to produce a generalized social network, we need to divide the original network into several sub-graphs each of which represented by an insensitive nodes, and the final generalized network should be also represented by these insensitive nodes.
K-nearest neighbor (KNN) method
Given a social network G with K insensitive nodes KNN method divides G into K sub-graphs by assigning each node v to its nearest insensitive node. Let SPD(v, viC) be the distance of the shortest path between v and viC. Starting from the sensitive nodes adjacent to insensitive node, KNN method assign sensitive node, one node per time, to the closest sub-graph Gi where SPD(v, viC) is shorter than or equal to SPD(v, vjC) where j = 1, 2, .., K and j ≠ i. After dividing a social network into K sub-graphs, we collapse all nodes of a sub-graph into one generalized node, and represent this node with the identity of the insensitive node of this sub-graph. Finally, for each possible pair of generalized nodes, say Gi and Gj in the generalized graph G', an edge will be created if and only if there is one or more edges between any two nodes in G from sub-graph Gi and Gj respectively.
Figure 2 presents a simple example to illustrate the idea of KNN and show how it works to produce generalized social network. Figure 2 (a) is the given social network which has seven nodes. Among them, v1 and v2 are insensitive nodes while the others are all sensitive nodes. By using KNN method, the given social network will be divided into two isolated social networks as shown in Figure 2 (b). Finally, one sub-graph is represented by v1 and another sub-graph is represented by v2, Figure 2 (c) demonstrates the final generalized social network where two generalized nodes are connected together because v4 and v5 are connected in G. The KNN subgraph generation algorithm is presented below:
length = 1;
V = V - {v1C, v2C, … vKC};
While V ≠ Ø
For each vj ∈ V
For each i = 1 to K
IF(SPD(vj, viC) == length);
Vi = Vi + vj;
V = V – vj;
End For;
End For;
End While
For each (vi,vj) ∈ E
IF(Subgraph( vi) == Subgraph( vj))
//Subgraph( vi) is the subgraph such that vi ∈ Subgraph( vi)
Gk = Subgraph( vi)
Ek = Ek + (vi,vj)
Create an edge between Subgraph( vi) and Subgraph( vj) and add it to E'
End For
Figure 2 . Illustrations of generating subgraphs.
Edge betweenness based (EBB) method
Instead of assigning sensitive nodes to the closest sub-graphs represented by insensitive nodes, the EBB method progressively remove edges with the highest betweenness and it also ensure that each separated sub-graph contains exactly one insensitive node. The betweenness of an edge is defined as the number of shortest paths between pairs of nodes that pass through it. If a network consists of a few of dense communities which are only loosely connected by some inter-community edges, these inter-community edges will have high betweenness, so that removing them will naturally break the social network into multiple communities. The EBB algorithm is presented as follows:
//EBB(G), Edge Betweenness Based method
Initialize e = {};
While(there are more than one insensitive node in graph G)
Identify edge (vi,vj) in G which is not an element of e and has the highest betweenness;
Remove (vi,vj) from G;
IF(G is still connected after removing edges (vi,vj))
ELSE IF (G is disconnected and split to two graph Gp and Gq)
IF(No insensitive node in Gp) or (No insensitive node in Gq)
Add (vi,vj) back to G;
e = e + (vi,vj);
Go Back to Step 2;
End While;
//Add edge between generalized node to form generalized graph
For each (vj,vj) ∈ E
IF(Subgraph( vi) == Subgraph( vj))
Gk = Subgraph( vi)
Ek = Ek + (vi,vj)
Create an edge between Subgraph( vi) and Subgraph( vj) and add it to E'
End For
Figure 3 shows an example of how EBB method works to produce generalized social network. Given a social network with nine nodes, v1 and v2 are insensitive nodes while all other nodes are sensitive nodes. Since edge (v1, v2) has the highest Betweenness and it is safe to be removed, EBB method delete this edge to form two separated sub-graphs each of them contains exactly one insensitive node, as shown in Figure 3 (b). Finally, the EBB method generalizes these two sub-graphs into two generalized nodes, and then connects them to form the generalized graph as shown in Figure 3 (c).
Figure 3 . Illustrations of generalizing subgraph byEBB.
Generalized sub-graph information
Given a generalized social network Gi and its center viC, we select shareable network properties based on the information need and the privacy policy. In this paper, we treat node identity as sensitive information that we should protect, and consider distance between nodes to be useful information for SNAM task. Let va and vb be any two nodes in Gi and the length of the shortest path between va and vb be SPD(vp,vq,Gi). We define the longest length of the shortest paths between any two nodes in Gi, denoted by L_SPD(Gi), as
We also define the shortest length of the shortest paths between any two nodes in Gi, denoted by S_SPD(Gi), as
To reduce the risk of releasing sensitive information, instead of sharing exact information of shortest path, we propose to share the expected length between two nodes within a generalized social network. Formally speaking, the length of any shortest paths in Gi, α, must be smaller or equal to L_SPD(Gi) and larger or equal to S_SPD(Gi), where S_SPD(Gi) ≤ α ≤ L_SPD(Gi). We compute and share the probability of the length of the shortest path between any two nodes in Gi, denoted as Prob(SPD(Gi) = α), and 0 ≤ Prob(SPD(Gi) = α) ≤ 1
Similarly, let the length of the shortest path between va and viC, be SPD(va,viC,Gi). We define the longest length of the shortest paths between viC and other nodes within Gi, denoted by L_SPD(viC,Gi), as
We also define the shortest length of the shortest paths between viC and other nodes within Gi, denoted by and S_SPD(viC,Gi), as
Since the length of shortest paths between viC and any other nodes in Gi must be smaller or equal to L_SPD(viC, Gi) and larger or equal to S_SPD(viC, Gi), denoted as S_SPD(viC, Gi) ≤ β ≤ L_SPD(viC, Gi). We compute the probability of the length of the shortest path between any node and viC, Prob(SPD(viC,Gi) = β), where 0 ≤ Prob(SPD(Gi) = α) ≤ 1.
We also denote Num(Gi) as the number of nodes in Gi and Num(Gi,Gj) as the number of nodes in Gi that are adjacent to another subgraph Gj.
The generalized subgraph information for sharing includes: (i) L_SPD(Gi), (ii) S_SPD(Gi), (iii) Prob(SPD(Gi) = α), (iv) L_SPD(viC,Gi), (v) S_SPD(viC,Gi), (vi) Prob(SPD(viC,Gi) = β), (vii) Num(Gi), and (viii) Num(Gi,Gj).
Generalized graph integration and social network analysis
In section 4.2 and 4.3 we introduced how to divide a social network into sub-graphs, and then generalize these sub-graphs to nodes, then finally produce a generalized social network. We also discussed what kind of information will be shared along with the generalized social network. In this section, given a generalized social network G' and the shareable information of the sub-graphs of G', we propose our own techniques to integrate social network and shared information to improve the performance of SNAM task.
Suppose organization Op has a social network Gp and organization OQ has another social network GQ, Op wants to integrate GQ with its own Gp to compute more accurate closeness centrality. We propose to achieve this goal without violating the privacy policies in three steps: (1) produce generalized social network and ; (2) integrate and into GIntegrated; (3) estimate the distance between any two nodes of the integrated social network. Among these three steps, step one can be achieved by our proposed techniques in section 4.2 and 4.3. In step two, although the sub-graphs represented by a common insensitive node in and are different and the connectivity between these insensitive nodes are also different, according to our proposed techniques, and are represented by the same group of insensitive nodes since Gp and GQ share same insensitive nodes. As a result, we can combine and into GIntegrated by taking union of their edges. In this section, we focus on the step 3 which estimate distances between any two nodes based on Gp, GIntegrated and shared information of sub-graphs of .
To re-estimate the distance between two nodes vi and vp of Gp by making use of GIntegrated and the shared information of sub-graphs of , we first identify the two closest insensitive nodes for vi and vj in Gp, and then use GIntegrated and the generalized information of to re-estimate their distances. Formally speaking, let the closest insensitive node to vi in Gp be , and the second closest insensitive node to vi in Gp be . We set the weights λA and λA' as
with and the weight of the closest insensitive node is higher.
Similarly, let the closest insensitive node to vj in GP be , and the second closest insensitive node to vj in GP be , we set the weights λB and λB' as
In GIntegrated, , and are the centers of generalized sub-graphs GA, GA, GB, and GB, respectively. We estimate the distance between vi and vj, d(vi,vj), by integrating the estimated distances of the four possible paths going through these insensitive nodes by a linear combination with weights equal to λα × λb.
D ( vi, vj) is the estimated distance between vi and vj on the path going through and , where a can be A or A' and b can be B or B'.
where Gk is a generalized node on the shortest path between Gα and Gb in GIntegrated If a ≠ b which means viand vj are not in the same subgraph, then D( vi, vj) is estimated by , and E( Gk). Otherwise, if viand vj are in the same subgraph then a = b. In this case, D ( vi, vj) is estimated by . corresponds to the expected length of the distance between vi and the sub-graph gatekeeper within Gα. Similarly, corresponds to the expected length of the distance between vj and the sub-graph gatekeeper within Gb. In addition, E ( Gk) is the expected length of the distance between any two nodes of sub-graph Gk that the shortest path between vi and vj is going through. If vi is not the same as is computed by E ( Gα) and the percentage of nodes in Ga that is adjacent to the sub-graph that is immediately following Ga in the shortest path between vi and vj in GIntegrated. If vi is the same as is equal to the expected length of the distance between the insensitive node, , to the other nodes in Ga. Computation of is done similarly.
where is the percentage of nodes in Gα as a gatekeeper which is adjacent to Gk and Gk is the sub-graph that immediately follows Gα in the shortest path between vi and vj in GIntegrated.
E ( Gk) represents the expected length of the distance between any two nodes of the sub-graph Gk, which is computed as:
D" (vi,vj) corresponds to the estimated distance between vi and vj when both vi and vj are nodes of the same sub-graph. In this case, if any of vi or vj is the same as , D" (vi,vj) should equal to the expected length of the distance from the insensitive node to the other nodes in, Gα. Otherwise, D" (vi,vj) should equal to the expected length of the distance between two nodes of the sub-graph.
Experiment and discussion
Practically, there isn't any intelligence unit has a complete terrorist social network but each of them has a partial terrorist social network. The objective of this work is to support these intelligence units to share their social networks while preserving the sensitive information. In this section, we investigated our proposed techniques on a real-world dataset of terrorists. We extracted several social networks from the terrorist dataset to simulate the real-world problem. Intensive experiment was conducted under different settings to evaluate our proposed techniques.
In this work, we employed the Global Salafi Jihad terrorist social network, denoted as G, in our experiment. The Global Salafi Jihad terrorist social network consists of 366 nodes (terrorists) and 1,275 edges (connection between terrorists)[23]. These terrorists come from four major groups, including Central Staff of al Qaeda (CSQ), Core Arab (CA), Southeast Asia (SA), and Maghreb Arab (MA). We randomly sample α percent of nodes from the Global Salafi Jihad terrorist social network as insensitive nodes, that their identities are known by all organizations. Suppose there are two independent organizations OP and OQ, we simulate GP for OP by randomly removing β percent of edges from the Global Salafi Jihad terrorist social network. Similarly, we randomly remove β percent of edges from the Global Salafi Jihad terrorist social network to simulate GQ for OQ. As a result, both GP and GQ are partial graph of G. Moreover, GP are different from GQ in terms of their edges.
As discussed before, there is no generic approach for privacy preservation since sensitive information can be defined in various ways. Moreover, shareable useful information is also different in terms of different SNAM tasks. In this work, we treat node identity as sensitive information and consider distance between nodes as useful information that we want to maintain. To evaluate our proposed technique, we assume that the SNAM task conducted by GP is to compute closeness centrality for each node. If GP is close to G, then distances between any two nodes in GP should be roughly equal to their distance in G, leading to similar closeness centrality for each node. Otherwise, nodes in GP should have different closeness centrality in G. In this work, closeness centrality for a node in GP is computed as:
where n is the total number of nodes in GP.
Given a complete social network G and the integrated social network GIntegrated, the performance of our proposed technique is evaluated by the error function defined as:
Figure 4 demonstrates the average closeness centrality of nodes of original graph (G), integrated graph using EBB method ( GIntegrated (EBB)), integrated graph using KNN method ( GIntegrated (KNN)) and incomplete graph ( GP). In Figure 4, the blue line represents the average closeness centrality computed from G, which is a gold standard, so that the closer to this blue line the better it is.
Figure 4 . Average closeness centrality of complete graph (G), integrated graph using EBB method, integrated graph using KNN methodand incomplete graphGP: (a) α = 0.05, (b) α = 0.15, (c) α = 0.25, (d) α = 0.35, (e) α = 0.45, (f) α = 0.55 (g) α = 0.65, (h) α = 0.75, (i) α = 0.85, (j) α = 0.95.
For each α from 0.05 to 0.95, we increased β (percentage of edges randomly removed from G) from 0.2 to 0.8. We observed that the performance of GP (GIntegrated (KNN)) and ( GIntegrated (EBB)) decreased consistently when more edges are removed from the complete graph, no matter what the value of α is. Although our proposed technique integrates networks and estimates the average closeness centrality, the performance will not be as good as the average closeness centrality computed from the actual graph G. When more edges are removed before integration ( β increase), the performance will degrade.
We further investigated the performance of our proposed technique by increasing the percentage of insensitive nodes from 0.05 (Figure 4(a)) to 0.95 (Figure 4(j)). Similar patterns are observed from 4(a) to 4(j). In terms of average closeness centrality, increasing or decreasing the percentage of insensitive nodes in network did not make substantial impact to the performance of our purposed technique. One plausible explanation is that: the average closeness centrality used in this experiment only reflects the performance of our approach in an abstract level. Some nodes in the integrated network may have higher closeness centrality than its original closeness centrality in the complete graph while some nodes may have lower closeness centrality in the integrated network than in the complete graph. As a result, when we consider the average closeness centrality, the differences may be offset by each other.
Figure 5 (a) presents the error ratio of (GIntegrated (KNN)) with different α and β. Similarly, Figure 5 (b) presents the error ratio of GIntegrated (EBB) with different α and β. We compute the errors in closeness centrality obtained from the networks with and without integration ( Error( Gintegrated) and Error( Gp)) using the error function defined in 5.2. and the error ratio is defined as:
Figure 5. (a) error ratio ofwith different settings ofβand α; (b) error ratio ofwith different settings ofβand α.
Different from the average closeness centrality which we used as a measurement in Figure 4, the error function accumulates the closeness centrality difference for each individual node, so that the offset effect of average closeness will not occur. The experiment results of Figure 5 can be used to verify our explanation to the Figure 4 in the last paragraph.
The experiment results demonstrate that when α is high (means more insensitive nodes), the improvement of our proposed technique comparing to the partial graph is also higher. The highest improvement was achieved when α equals to 0.95. The improvement decreased slowly along with the decrease of α. This observation indicated that our explanation of Figure 4 is correct. With more insensitive nodes, the integrated network will be closer to the original network so that the improvement of our technique will be higher.
Last but not least, from both Figures 4 and 5, we do not observe any significant differences of the performance between using KNN or EBB to produce generalized network. However, as it is shown in section 4.2.2, the EBB algorithm is dominated by the step of calculating the edge betweenness which has time complexity O(N3). On the other hand, KNN is much more efficient which is only O(N). As a result, when the network size is huge, KNN is preferred. Moreover, in a fully connected network where several edges have the same betweenness weight, EBB will take longer to produce the generalized network. However, KNN also has its limitation. For example, KNN starts from each insensitive node to look for sensitive nodes in its neighborhood to form a sub-graph step by step. However, the search process is not fully simultaneous, but is controlled by a FOR loop. As a result, the sequence in the FOR loop is matter, especially for some nodes in the middle of two insensitive nodes. As a result, the division of sub-graph by using KNN is less natural than EBB method.
In this paper, we investigate the privacy preservation techniques for social network integration. We introduce a research framework which consists of three major steps. First of all, we propose the K-Nearest Neighborhood method and the Edge Betweenness Based method to decompose a social network into multiple sub-graphs. Secondly, we propose techniques to generalize a social network by sharing the probabilistic model of the generalized information. At third, we introduced the techniques of social network integration and distance estimation.
Using the Global Salafi Jihad terrorist social network as test bed, we thoroughly evaluated our proposed technique with different parameters and settings. The experiment results demonstrated that an organization can improve the accuracy of computing closeness centrality by sharing and integrating generalized information. Our proposed techniques were able to preserve the privacy as well as increase the utility of the shared social networks. We observed that KNN performed better than EBB but did not have substantial difference. Moreover, our proposed techniques were not sensitive to the number of insensitive node but relatively sensitive to the number of removed edges.
In the future, we will continue to examine our techniques in more datasets. We will explore other graph partition models and integration techniques to improve the performance of our technique. Moreover, we will also extend our work to maintain other useful information besides distance.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
CCY proposed the research framework. CCY and XT developed the algorithms together. XT implemented the algorithms and conducted experiments. CCY and XT together drafted the manuscript. All authors read and approved the final manuscript.
Yang CC, Sageman M: Analysis of terrorist social networks with fractal views.
J Inf Sci 2009, 35(3):299-320. Publisher Full Text
Yang CC, Ng T: "Terrorism and crime related weblog social network: Link, content analysis and information visualization,".
Yang CC, Liu N, Sageman M: "Analyzing the terrorist social networks with visualization tools,".
Yang CC, Tang X: Social networks integration and privacy preservation using subgraph generalization. In Proceedings of the ACM SIGKDD Workshop on CyberSecurity and Intelligence Informatics. ACM, ; 53-61.
Yang CC, Tang X: Information Integration for Terrorist or Criminal Social Networks.
Ann Inform Syst 2010, 9:41-57. Publisher Full Text
Tang X, Yang CC: "Generalizing terrorist social networks with K-nearest neighbor and edge betweeness for social network integration and privacy preservation,".
Yang CC, Thuraisingham B: Privacy-Preserved Social Network Integration and Analysis for Security Informatics.
Campan A, Truta T: "A clustering approach for data and structural anonymity in social networks,".
Proceeding of the second ACM SIGKDD International Workshop on Privacy, Security, and Trust in KDD
Hay M, Miklau G, Jensen D, Weis P, Srivastava S: "Anonymizing social networks,". University of Massachusetts Technical Report, ; 2007:07-19.
K. Liu, and E. Terzi, "Towards identity anonymization on graphs", Proceedings of the: ACM SIGMOD international conference on Management of data. ACM New York, NY, USA; 2008:93-106.
Hay M, Miklau G, Jensen D, Towsley D, Weis P: Resisting structural re-identification in anonymized social networks.
Zhou B, Pei J: "Preserving privacy in social networks against neighborhood attacks,". In IEEE 24th International Conference on Data Engineering. ICDE, ; 2008:506-515.
Zheleva E, Getoor L: Preserving the privacy of sensitive relationships in graph data.
Backstrom L, Dwork C, Kleinberg J: "Wherefore art thou r3579x?: anonymized social networks, hidden patterns, and structural steganography,". In Proceedings of the 16th international conference on World Wide Web. ACM New York, NY, USA; 181-190.
Cormode G, Srivastava D, Yu T, Zhang Q: Anonymizing bipartite graph data using safe groupings.
"Randomizing social networks: a spectrum preserving approach," SIAM Conf. on Data Mining. .
Sweeney L: k-anonymity: A model for protecting privacy.
Int J Uncertainty Fuzziness Knowledge Based Syst 2002, 10(5):557-570. Publisher Full Text
Samarati P: "Protecting respondents' identities in microdata release,".
Machanavajjhala A, Kifer D, Gehrke J, Venkitasubramaniam M: "l-diversity: Privacy beyond k-anonymity".
ACM Trans Knowledge Discov Data (TKDD) 2007, 1(1):3. Publisher Full Text
X. Xiao, and Y. Tao, "Personalized privacy preservation", Proceedings of the: ACM SIGMOD international conference on Management of data. ACM New York, NY, USA; 2006:229-240.
Wong R, Li J, Fu A, Wang K: "(alpha, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing,". ACM Press, ; 754-759.
Frikken K, Golle P: "Private social network analysis: How to assemble pieces of a graph privately,". ACM New York, NY, USA; :89-98.
Sageman M: Understanding Terror Networks. University of Pennsylvania Press, ; 2004.

subset: commoncrawl


text:
Individuals who love animals will love being a Foster Parent Volunteer at The Humane Society of Central Oregon! Opening your heart and home to provide temporary care for a cat, kitten, dog or puppy is a tremendously rewarding experience for both the foster parent and the foster animal.
Foster Supplies Needed:
- Kitten Milk Replacement
- Neonate bottles
- Chicken or Turkey based soft kitten food
- Digital thermometers
- Esbilac
Foster Parent Volunteers are an integral part of HSCO mission of saving animal lives. Countless numbers of kittens, puppies, cats and dogs receive a temporary home and wonderful care from our dedicated team of foster parents until they are ready to be adopted into a permanent home.
Some animals may only need a home for several days while others may need several months of care. Foster animals are carefully matched with foster homes according to their needs and the foster parent abilities.
Fostering is a great way to bring a furry companion into your life when you are not quite ready to make a lifetime commitment. Fostering also allows you to volunteer from the comfort of your own home. You will be enriching their lives as well as yours! Download our Foster Family Application below.
For more information contact Karen Burns or Juliana Griffith at 541/382-3537 or Email us here.
You can also see our Wish List of Foster Home Care Supplies that are needed here.


text:
All Your Mobile Devices Accessory Needs
I love buying things online, not only because it's cheaper, it's because I can take my own sweet time to decide about it. I just hate the feeling when the sales assistant standing beside waiting for you to choose, be it clothes, accessories, and even lingerie.
So, I'm glad that my friend have opened up a Facebook page selling mobile devices accessories called JameZco Mart. Good thing about this page is that he posted the actual photos of the items, rather than those much photo-shopped photos. So, at least you know what to expect when you get the items.
I hated my cables all tangle up, especially my earphones. I bought a earphone winder from elsewhere before but that was totally useless, it couldn't even fit all the cables in, that means the rest of the cables are not being winded up and gets tangle. =,=""
Then I saw this super duper cute earphone winder and I know I gotta own it!!!
That's Jacky and Blacky.
Okie lah maybe coz I love dogs a lot. But seriously, cute right? Jacky looks cute and innocent and Blacky looks so cool! Unlike the one I bought last time, this earphone winder have enough space for the wires and it also acts like a fashion accessory when you're using it.
Oh, and here's the actual photo of the item.
I needed a new micro USB cable as my old one is faulty already. Since I'm gonna get a new cable so I thought why not I get this cutesy colourful looking Noodle / Flat micro USB cable, RM12 for data transfer + charging.
He does have a cheaper price ones at only RM6 but it can only be used for charging only.
The colour darn nice right? I got myself the pink ones!
Oh, u're using iPhone? Fret not, he has the noodle cables for iPhone as well!
If you have those external backup battery charger for phone, u might want to get this cable to use with it, at least it looks nice when u're using ur phone while charging, rather than the plain bored looking black cable.
There's also screen protector for the two famous latest phone models which is iPhone 5 and Samsung Galaxy Note 2.
He even posted up a video on how to stick on the screen protector since you gonna do it on your own.
Okla nuff said, go check out his Facebook page here!


text:
Advertisement - Continue Reading Below
Deal With An Ex Video
Anique tells you how to deal with an ex boyfriend on campus.
This Website Used a "Rape Us Now" Slogan to Promote Its Black Friday Sale
Whoever thought this was a good idea is seriously disturbed.
I Was One of the Country's Top Surfers Until a Devastating Concussion Ended Everything
Harley Taich was heading toward professional surfing stardom until a head injury at 15 left her bedridden for 18 months, and effectively...
The Emotional Reunion Between This Teen & the Doctor Who Saved Her Life as a Baby Will Make You Cry
Dr. Robert Goldstein performed a life-saving liver transplant on Ashli Taylor when she was an infant.
More From Life


text:
MIP-TV News: TV-Loonland Hypes Heidi Movie For MIP-TV
At this year's MIP-TV, TV-Loonland will show new developments such as WEREKIDS and JOHNNY MUTTON and will also bring a host other shows such as HEIDI, a 75-minute feature version of the classic children's tale.
Set in the Swiss mountains, HEIDI will be true to the popular Heidi fables, paying homage to the strong, colorful personalities the books. David Ferguson, coo of TV-Loonland, said, "HEIDI is a remarkably popular story that generations of kids around the world have grown up with. TV-Loonland and Nelvana are pooling our creative strengths to produce a refreshing new version of this classic that will reflect the more sophisticated animation techniques of today."
WEREKIDS, a 26x11 series in development for kids 7-11, follows five supernaturally gifted school kids who battle the forces of the paranormal. The trailer and pilot script will be available at MIP-TV.
JOHNNY MUTTON, a 26x1 series in development for 6-11 year olds, is based on THE ADVENTURES OF JOHNNY MUTTON from children's book author/illustrator James Proimos. The story follows Johnny, a sheep being raised as a human boy. The trailer and pilot script will be available at MIP-TV.
In addition, TV-Loonland will bring a second season of THE CRAMP TWINS, a 26x30 series aimed at children 7-11. The first season was a number one hit in the U.K. on both Cartoon Network and CBBC, and it has since been sold to more than 100 television stations worldwide, including 4Kids Ent.'s Saturday morning FOX BOX lineup in the U.S. The new season has already begun delivering.
MIP-TV will also see TV-Loonland's first CGI series, DRAGON'S ROCK (26 x 22). A co-production with Germany's Super-RTL, the show tells the story of dragon family the Hoppers, a regular family of five just trying to make ends meet and cope with the chaos of daily life. Aimed at family audience, TV-Loonland has all 26 episodes available.
TV-Loonland will also show a number of new pilot series, including RUDI AND TRUDI (26 x 11) and SAM HAMWICH, SUPERHERO SANDWICH (26 x 11). RUDI AND TRUDI is a pre-school series featuring the mischievous raven Rudi and his best friend Trudi the kitten. SAM HAMWICH, SUPERHERO SANDWICH is aimed at kids 7-12 and follows the comic adventures of Superhero Sam as he takes on unappetizing "evil doers." Both have trailers and pilot scripts ready for viewing.
TV-Loonland (www.tv-loonland.com) is an integrated media company producing and distributing internationally animation for children, youth and families. With its own development teams and production facilities in Europe, Great Britain, Asia and the U.S.A., a comprehensive rights catalog as well as a focused distribution effort for Europe, North and South America and Asia; TV-Loonland has become a world leader among the independent majors in the industry.


text:
The Afghan War Diary (AWD for short) consists of messages from several important US military communications systems. The messaging systems have changed over time; as such reporting standards and message format have changed as well. This reading guide tries to provide some helpful hints on interpretation and understanding of the messages contained in the AWD.
Most of the messages follow a pre-set structure that is designed to make automated processing of the contents easier. It is best to think of the messages in the terms of an overall collective logbook of the Afghan war. The AWD contains the relevant events, occurrences and intelligence experiences of the military, shared among many recipients. The basic idea is that all the messages taken together should provide a full picture of a days important events, intelligence, warnings, and other statistics. Each unit, outpost, convoy, or other military action generates report about relevant daily events. The range of topics is rather wide: Improvised Explosives Devices encountered, offensive operations, taking enemy fire, engagement with possible hostile forces, talking with village elders, numbers of wounded, dead, and detained, kidnappings, broader intelligence information and explicit threat warnings from intercepted radio communications, local informers or the afghan police. It also includes day to day complaints about lack of equipment and supplies.
The description of events in the messages is often rather short and terse. To grasp the reporting style, it is helpful to understand the conditions under which the messages are composed and sent. Often they come from field units who have been under fire or under other stressful conditions all day and see the report-writing as nasty paperwork, that needs to be completed with little apparent benefit to expect. So the reporting is kept to the necessary minimum, with as little type-work as possible. The field units also need to expect questions from higher up or disciplinary measures for events recorded in the messages, so they will tend to gloss over violations of rules of engagement and other problematic behavior; the reports are often detailed when discussing actions or interactions by enemy forces. Once it is in the AWD messages, it is officially part of the record - it is subject to analysis and scrutiny. The truthfulness and completeness especially of descriptions of events must always be carefully considered. Circumstances that completely change the meaning of an reported event may have been omitted.
The reports need to answer the critical questions: Who, When, Where, What, With whom, by what Means and Why. The AWD messages are not addressed to individuals but to groups of recipients that are fulfilling certain functions, such as duty officers in a certain region. The systems where the messages originate perform distribution based on criteria like region, classification level and other information. The goal of distribution is to provide those with access and the need to know, all of the information that relevant to their duties. In practice, this seems to be working imperfectly. The messages contain geo-location information in the forms of latitude-longitude, military grid coordinates and region.
The messages contain a large number of abbreviations that are essential to understanding its contents. When browsing through the messages, underlined abbreviations pop up an little explanation, when the mouse is hovering over it. The meanings and use of some shorthands have changed over time, others are sometimes ambiguous or have several meanings that are used depending on context, region or reporting unit. If you discover the meaning of a so far unresolved acronym or abbreviations, or if you have corrections, please submit them to email@example.com.
An especially helpful reference to names of military units and task-forces and their respective responsibilities can be found at http://www.globalsecurity.org/military/ops/enduring-freedom.htm
The site also contains a list of bases, airfields http://www.globalsecurity.org/military/facility/afghanistan.htm Location names are also often shortened to three-character acronyms.
Messages may contain date and time information. Dates are mostly presented in either US numeric form (Year-Month-Day, e.g. 2009-09-04) or various Euro-style shorthands (Day-Month-Year, e.g. 2 Jan 04 or 02-Jan-04 or 2jan04 etc.).
Times are frequently noted with a time-zone identifier behind the time, e.g. "09:32Z". Most common are Z (Zulu Time, aka. UTC time zone), D (Delta Time, aka. UTC + 4 hours) and B (Bravo Time, aka UTC + 2 hours). A full list off time zones can be found here: http://www.timeanddate.com/library/abbreviations/timezones/military/
Other times are noted without any time zone identifier at all. The Afghanistan time zone is AFT (UTC + 4:30), which may complicate things further if you are looking up messages based on local time.
Finding messages relating to known events may be complicated by date and time zone shifting; if the event is in the night or early morning, it may cause a report to appear to be be misfiled. It is advisable to always look through messages before and on the proceeding day for any event.
David Leigh, the Guardian's investigations editor, explains the online tools they have created to help you understand the secret US military files on the war in Afghanistan: http://www.guardian.co.uk/world/datablog/video/2010/jul/25/afghanistan-war-logs-video-tutorial
|Reference ID
|2007-02-06 06:06
|Suspicious Incident
|Host nation
|Killed in action
|Wounded in action
At 060624ZFEB07 TF Titan reports troops in contact based on imminent threat. Intel indicates 3x enemy pax are conducting an illegal checkpoint IVO the location of the 2x Hilux trucks that were set on fire yesterday, approximately 10km east of Kamdesh. A TF Titan element moved to the location (YE 21927 21488), and confirmed there were 2x Hilux trucks on fire(1x blocking the road and 1x that could be used to block the road). 1x Hilux had cargo in it (but could not tell what it was due to bunring). TF Titan cleared both vehicle off the road into the river, and sent ANA patrols to clear high ground where suspected fighting positions are located. ANP will continue to investigate. NFTR. ISAF Tracking # 02-085
Report key: E97DF682-269C-4BE1-B006-920C2ADAA130
Tracking number: 2007-038-044919-0987
Attack on: ENEMY
Complex atack: FALSE
Reporting unit: TF 3-71
Unit name: TF 3-71
Type of unit: None Selected
Originator group: UNKNOWN
Updated by group: UNKNOWN
DColor: RED


text:
Monthly Archives: October 2008
This is Halloween, everybody scream!
Some of you may know that I love Halloween probably just a little more than any other holiday. It may have something to do with the colors, or the feeling of Fall, or the smell of warm fires in the air. Or it's the pumpkins, because I have some kind of love affair with those magical gourds of all shapes and colors. I think because I hate scary movies and have issues with terror and such, this allows me to get close to "scary" things but not actually be mentally messed up.
Tonight, Elliot will dress as The Cutest Pumpkin (official term) and we're going to Trick Or Treat with a group of my friends and their kids. Unfortunately, Ash is still harboring this awful plague so he's going to stay home and (presumably) hand out the candy I bought to the kids we might get. I kind of have a feeling that we'll get more people at the door this year. It seems like the fear of door to door trick or treating may have lifted some. In years past, it's been a sad show around our neighbourhood.
I decided not to carve my pumpkins this year. I got two smallish ones and for one, I was going to carve pi, because, you know, pumpkin pi, hard-ee-har-har. But I just didn't have it in me. They looked so cute just side by side on my front porch. And besides, my carpal tunnel kills when I have to hold any kind of utensil for a while and carving would be the death of my right hand.
Here's a picture of Elliot from this morning. He's wearing one of his cuter outfits, handing me a ball and saying, "This?", as he so often does.
SO! I have a whole bunch of work to do before lunchtime, when I have to run up to Target for last minute holiday things. What are your plans for the evening?
- Went to a kids' consginment shop in town that always has great stuff. Found only ONE sleeper and later on in my car, found it to have a tear right at the crotch. Every time I have tried to sell something, they scrutinized it for minutes before deeming it worthy. Obviously not with this item.
- Have an assignment to do that I am DREADING, mostly because no one else in the group for it has done theirs either. Why should I start?
- I have no idea what Elliot is going to eat for dinner; he's been rejecting almost everything. I have a feeling it'll be cheese, yogurt and a banana. AGAIN.
- Ash is STILL sick and I'm a LEETLE bit OVER it. (Also, having a brief obsession with the caps lock.
- My jeans just formed a hole in them on my upper right thigh. Not happy about that.
- It's only 2:50.
- I have a headache
By the numbers
I have a problem: I obsess about money. Not in the way that say, young entrepreneurs obsess in that they want to make a ton of it. I simply want to be comfortable. But I am constantly worried about it, thinking about how much is left in my account and whether or not I can afford X during this pay cycle. To clarify, my husband and I split everything. For most* couples, this is not the case. If one makes more than the other, it does not matter for it all goes into one pot. Sometimes, I wish it were different but for the most part, I like having a seperate bank account. I don't really want to see all the money coming out each day Ash goes out to lunch and he doesn't need to see each latte I buy, Even though we both know what the other spends their money on, it's nicer not to have to see it coming out of OUR account.
Anyway, that's not the point of this post. The point is that I was thinking about how much "extra" money I used to have to buy stuff, like Halloween decorations. And I argue sometimes with people that kids "aren't that expensive". But I think if I tallied up how much I spend in one month on baby supplies, food, etc, I would see that there is where my fun money went. For example:
- Monthly spending at Target on things like diapers, baby food, and diaper genie refills: 110.00
- Monthly spending at Walgreens for Pedialyte in case of illness: 20.00
- Monthly amount in groceries spent on food for Elliot: Around 100.00
So you see, I could have about 230 extra dollars to buy decorations or a pedicure, etc. I am getting really good at going without, even though those are frivolities that make me seem selfish to want, they can also bring me small joys.
What do you go without so that someone else may have?
*I say MOST because I keep runing into more and more people who are splitting it evenly these days.
Help me win and I promise to share!
So I am going to see if I have the coolest WP blog (which, well, I think I do). So, leave me some comment love and I'm going to enter. If it helps you comment, why don't you tell me which blog/blogs you read most often? Then we all get a little something out of this.
Autumn plague, 54 days until Winter
So the ICK has been going around… and around; everyone is sick and some people are sick again, me included. My cough had dried up and then this morning, I am once more having drainage issues (Draaaaiiiinagggge!! "There Will Be Blood reference, anyone?). I wish this cursed affliction would leave my family the hell alone; I'm getting tired of this. Oh yeah, and Ash just left work to go to the store and buy OJ and vitamin C pills and he's currently getting cozy with the comforter on the couch, no doubt to take in hours upon hours of SportsCenter and nap away his illness. In some ways, I am kind of jealous. BUT, the lemonade stand is going to have hot chocolate today so that's a definitely plus to coming into work.
SO I have been thinking lately about this NaNoWriMo thing that's coming up – like, soon! I have never even considered taking on this ginormous feat: 50,000 words in one month, but I think I can. I didn't realize that, really, the only goal is QUANTITY, not QUALITY. Well, hell, I can pump out a phenomenal amount of CRAP, that's for sure. But will I want to? I am going to be editing like a fiend and deleting, repeating, rewriting and in general, probably spend a good portion of November feeling like this could be so much better. But we'll see. I find myself being very project driven so having this one thing throughout November might be really good for me. The question is: do I also take on NaBloPoMo?
And on an entirely unrelated note (not true – I'm trying to work this kind of stuff into my novel) I was thinking this morning (because I felt reminiscent about things) about this black tin my mother had. It bore a small flowery pattern wherein the gold petals were shiny. Inside, she kept all sorts of random things and these are the ones I remember: some patches from NASA, Care Bears, and YMCA, bobby pins, various sizes of safety pins, a few bobbins of thread, sewing needles of various eye sizes, a few random coins, a few flattened pennies that had been collected from the railroad tracks, an eyedropped with a black bulb no longer than my pinky, and a few buttons in different sizes and colors. For some reason, I loved when she got out that box because I liked looking at those items. I look forward to seeing if Elliot remembers anything like that. Do you have anything like that from your childhood?
Catch up
So this morning, I drove through the rurals of Southern Alabama to a teeny tiny town called Cottonwood, to renew the tag on my deceased grandfather's Suburban. My father is executor of the estate and the powers that be in probate gave him all this paperwork and said I'd definitely be able to do it. So my father pays me 100 dollars, I drive almost 2 hours through towns that don't even have a stop light, (all while missing work, mind you) all to find out that they will not renew the tag because a) my grandfather is dead and I didn't have a death certificate with me, b) I nor my father actually resides in the state of Alabama and c) the person who will inherit the vehicle lives in Kentucky and all state rules are different. Reluctantly, I got back in my car and came home. On my way out, I saw Cottonwood's little animal hospital, which was closed, but an English bulldog and a bassett hound were sort of milling about then pawing at the door, where a golden retriever was trying to get out. Very cute; it looked like they were sort of in a cartoon where the two dogs were trying to break free the other, and they had plans.
The weekend was not relaxing. It had its ups… and downs, but I was busy the entire time. Around 11:30 on Saturday, my friend, Desiree, called and offered us a couple tickets to the football game. So we went out to lunch (Longhorn) and then met Desiree, her husband, their 4 year old son, and her mother at the stadium. I must say, at least one of the perks of working at FSU is that I have faculty/staff access to 24 hour restricted parking lots. We still had to walk about 4 blocks but it wasn't insane like everyone else. It was Elliot's first FSU football game! And he did… ok. He really wanted to walk around but it was versus Virginia Tech and sold out. Fortunately, Desiree and her mom took turns with us taking him down to walk around near all the concessions, where it was also much cooler. I think it was only in the 70s but our seats were on the sunny side of the stadium. Elliot ended up falling asleep at one point so all was well in the end. Oh, and FSU won so I guess it was ALL good.
Sunday morning Ash woke up feeling sick again. He just can't seem to shake these flu-like symptoms. Personally, I'm getting a little tired of playing nurse. Elliot is, for the most part, feeling better, though he had a typical toddler day yesterday: oh I'm so happy, wait what? No, I hate you, whine whine cry. I had to take him out multiple times because that's the only way to keep him satiated. We went to the mall and Kohls and just around town so he'd shut up for five seconds. There was a point in the late afternoon where nothing would make him happy and he was all whiny and then Ash started getting all mopey and pitiful and I was like, "This house is full of assholes – I need to get out!". Though I didn't. I stuck it out until after bedtime, after working out, and then I went to CVS and bought those Sudacare vapor relase tabs and took a long hot shower.
And this morning, I got a pumpkin cream cheese muffin. Which I now must work off. ::grumble::
I got back into the office around 11 and I am swearing off taking any more time. I was out of here so much last week with Elliot being sick and I cannot afford this any more!
So that's what I've been doing. How about you?
What I owe you
I've been an awful blogger lately. You can blame it on being a mother with a cold, a sick kid, and a sick husband. It's just been ridiculous since we got back from Chicago. Sure, there was the 3-4 days right after when work was slow and I had some time to MYSELF – because if vacation isn't good for enough things, it will certainly remind you why you make time for yourself, away from your husband, sometimes. The past two days, we've both been home – because of teh sickness – and I am ready to high-tail it to the other side of the country, to some resort in the mountains where I can check my email in peace and maybe, you know, read one sentence in my book before someone needs some Nyquil or snot wiped from their ever-flowing nose or, or, or… you get the point! It's a never ending cycle in this household and I need a BREAK!
But really, this time of year – though I love it – can sometimes get me down. And so long as I am admitting things, I feel like I've been a not-so-great wife lately – just been kind of whiny and complainy about things. The very last way to get Ash to do anything is to whine about it. He works best if we're all working together. Like, if I do X, can you do Y? That's what gets him motivated. But I tend to get into a mood and be all like, "Why can't you just see that X needs to get done and do it? I know you know…" And then he shuts down. I mean, SHUTS DOWN. The system is down. And then I get all mad and think that somehow by getting all upset, it'll make him change. LOL. What have I been thinking?!? The worst part is when you know something is totally futile but you do it anyway. It's actually HARD to make a change, you know? I always thought it was as easy as saying "Ok well, I'll just not do that anymore" and POOF, things would be all better. Such is never the case. It takes hard work and acknowledging the problems, being on the look out for them, and catching them before they arise again.
SO, now that I have that off my chest, let's talk about fun stuff, ok?
We had a lot of fun at the Pumpkin Patch on Saturday, though I wish we could have stayed longer. Really, there's not a whole lot to do when he's this young though. He was walking around, touching the pumpkins, but he liked riding in the wagon the most. Have I ever told you the ONE thing I always wanted as a kid but never got? It was a Radio Flyer. I really really wanted one but I never did. Though, come to think of it, I didn't ask for it either. Looking back, I guess I just sort of expected my parents to buy me one. Anyway, E loved the wagon and he had so much fun.
Unfortunately on Sunday, his ear infection began. BUT, here's some things upcoming to look forward to:
- Cooler weather = ridiculously cute baby clothes.
- Cooler weather = Elliot is going to need new footies (Target anyone?)
- My parents are coming here for Thanksgiving and I get to be Martha in the kitchen
- Sundry is going to be launching a new site soon about fitness and as far as I know, my weight loss success story will be on it!
- Pregnancy magazine is starting a new online site in which all moms who have submitted their birth story for publication will be included. This means that mine from the September '08 issue will be there.
- For Christmas, we're going to Orlando. But we'll also be going to Tampa for my friend's wedding and then to Palm Beach to see my grandparents.
SO, if you're still around and reading this godforsaken blog, tell me: what do you have coming up that's exciting?
Teh suck
- I still have a very nasty congested cough
- Elliot has an ear infection
- We stayed home today
- In general, he's not unpleasant to be around but he's still sick – he smells like sick kid
- Ash is sick too – except he has some kind of stomach thing. Ew. He's dying on the couch right now
- I have to drive to the tiny town of Cottonwood, Alabama this coming Monday
- To deal with something involving my late grandfather, his old truck, and probate
- My father paid me to do this
- However, unexpected medical costs have blown that payment to bits
- Oh, it's still PMS week, in case you were wondering
- I could use about 3 glasses of wine and maybe a, um, Xanax? Yes please
Girly things, emotional babble
I'm a runner. Not this kind of runner but I tend to run and hide from problems or issues that are plagueing my brain/life. If I have something upcoming that I know might be a less than smooth discussion with Ash (holiday plans, money-related anything…) I tend to throw myself into other projects or go so far inside myself that I don't even realize that I am totally oblivious to a lot going on around me. It's sort of scary how that doesn't even seem like me.
Today, I feel sad and worried for no particular reason at all, except – I wager a guess – it's the week before "that special time of the month", which tends to be highly emotional for me. I must say, when I was breastfeeding and solely on the mini-pill, my mood swings were definitely milder than before. I am back on regular birth control and I can pretty much count on a "PMS" week, wherein I'll feel like everyone is forsaking me and my life is in shambles, even though – as far as I can tell – everythng is peachy. Maybe I just don't know how to deal with this sort of feeling. It's during this time I usually feel like I need a therapist. Or a lot of caffeine. Or lots of sleep. The thing is – and I hate to admit this – if I could just have a good cry, I would be totally fine.
Sometimes, I think if I were a guy I'd have to deal with less of this. Sure, I know Ash worries but it is rare he feels like this. Whenever I talk it out, he tries to understand and console but he deals much better with his emotions and ideas. He is less afraid than I.
Thanks for listening to me, even in my somewhat insane babble. Sometimes I just need to throw it all out there.


text:
Spicy Sesame Noodles
Source: Diabetic Cooking Magazine
Soba (buckwheat noodles) with sesame oil, peanut butter, and jalapeno pepper.
6 oz Pasta, soba, dry
2 tsp sesame oil
1 tbsp sesame seeds
0 1/2 cup fat free reduced sodium chicken broth
1 tbsp Peanut Butter, creamy
4 tsp low sodium soy sauce
0 1/2 cup fresh chopped green onion
0 1/2 cup chopped red bell peppers
1 1/2 tsp sliced jalapeno pepper
1 each medium garlic cloves
0 1/4 tsp hot red pepper flakes
ads keep dLife free
dLife Weekly Poll
What dietary strategies do you use to control your diabetes?
Sign in or Join dLife now to help keep track of your favorite recipes and foods.
ads keep dLife free