Taking into account of both the huge computing power of intruders and untrusted cloud servers, we develop an enhanced secure pseudonym scheme to protect the privacy of mobile cloud data. To face the huge computing power challenge, we develop an unconditionally secure lightweight network coding pseudonym scheme. For the privacy issue of untrusted cloud server, we further design a two tier network coding to decouple the stored mobile cloud data from the owner pseudonyms. Therefore, our proposed network coding based pseudonym scheme can simultaneously defend against attackers from both outside and inside. We implement our proposed two-tier light-weight network coding mechanism in a group location based service (LBS) using untrusted cloud database. Compared to computationally secure Hash-based pseudonym, our proposed scheme is not only unconditionally secure, but also can reduce more than 90 percent of processing time as well as 10 percent of energy consumption.
@cite_7: In many real life applications, the locations of events monitored by the network are required to remain anonymous. Preserving source location privacy becomes important, and also difficult to implement in case of wireless sensor networks (WSN) given their operating constraints. The privacy threats that exist for sensor networks may be categorized broadly into content privacy and context privacy. Context based privacy protection in WSN aims to hide the location information of the sensor nodes. This paper proposes a source location privacy scheme for WSN through cluster based anonymization. The scheme hides the real node identities during communication, by replacing them with random identities generated by the cluster heads. The degree of privacy of WSN is analyzed using entropy based method. The simulation results show that the scheme improves the degree of privacy compared to a method which does not have any provision of anonymity for sensor nodes.
@cite_1: This paper describes a personalized k-anonymity model for protecting location privacy against various privacy threats through location information sharing. Our model has two unique features. First, we provide a unified privacy personalization framework to support location k-anonymity for a wide range of users with context-sensitive personalized privacy requirements. This framework enables each mobile node to specify the minimum level of anonymity it desires as well as the maximum temporal and spatial resolutions it is willing to tolerate when requesting for k-anonymity preserving location-based services (LBSs). Second, we devise an efficient message perturbation engine which runs by the location protection broker on a trusted server and performs location anonymization on mobile users' LBS request messages, such as identity removal and spatio-temporal cloaking of location information. We develop a suite of scalable and yet efficient spatio-temporal cloaking algorithms, called CliqueCloak algorithms, to provide high quality personalized location k-anonymity, aiming at avoiding or reducing known location privacy threats before forwarding requests to LBS provider(s). The effectiveness of our CliqueCloak algorithms is studied under various conditions using realistic location data synthetically generated using real road maps and traffic volume data
@cite_32: Advances in sensing and tracking technology enable location-based applications but they also create significant privacy risks. Anonymity can provide a high degree of privacy, save service users from dealing with service providers’ privacy policies, and reduce the service providers’ requirements for safeguarding private information. However, guaranteeing anonymous usage of location-based services requires that the precise location information transmitted by a user cannot be easily used to re-identify the subject. This paper presents a middleware architecture and algorithms that can be used by a centralized location broker service. The adaptive algorithms adjust the resolution of location information along spatial or temporal dimensions to meet specified anonymity constraints based on the entities who may be using location services within a given area. Using a model based on automotive traffic counts and cartographic material, we estimate the realistically expected spatial resolution for different anonymity constraints. The median resolution generated by our algorithms is 125 meters. Thus, anonymous location-based requests for urban areas would have the same accuracy currently needed for E-911 services; this would provide sufficient resolution for wayfinding, automated bus routing services and similar location-dependent services.
@cite_43: 
@cite_27: 
@cite_17: Recently, highly accurate positioning devices enables us to provide various types of location based services (LBS). On the other hand, because such positioning data include deeply personal information, the protection of location privacy is one of the most significant problems in LBS. Lots of different techniques for securing the location privacy have been proposed, for instance the concept of Silent period, the concept of Dummy node, and the concept of Cloaking-region. However, many of these researches have a problem that quality of the LBS (QoS) decreased when anonymity is improved, and anonymity falls down when QoS is improved. In this paper, we present a node density-based location privacy scheme which can provide location privacy by utilizing hybrid concept of Dummy node and Cloaking-region. Simulation results show that the probability of tracking of a target node by an adversary is reduced and the QoS of LBS is also improved.
Anonymization techniques can protect location privacy by generalization and suppression techniques, i.e., with an expression of lower granularity. In @cite_32 the @math -anonymity concept was introduced so that a user's location cannot be distinguished from other @math users' locations. Instead of reporting the exact location, a user sends a region containing the locations of other @math people. In @cite_1 , the CliqueCloak algorithm was proposed to provide different @math -anonymous requirements for each user. A privacy-preserving architecture for LBS with different anonymization techniques was suggested in @cite_43 . A node density-based location privacy scheme was proposed to improve anonymity without degrading quality of service (QoS) @cite_17 . The authors of @cite_7 proposed a cluster based anonymization scheme to replace the real node identities with random identities generated by the cluster heads. In @cite_27 , a clustering anonymization for vehicular networks was developed to hide the road and traffic information. However, all the above anonymization techniques cannot protect location data shared by a group of users since an individual's location is concealed.