As a significant business paradigm, many online information platforms have emerged to satisfy society's needs for person-specific data, where a service provider collects raw data from data contributors, and then offers value-added data services to data consumers. However, in the data trading layer, the data consumers face a pressing problem, i.e., how to verify whether the service provider has truthfully collected and processed data? Furthermore, the data contributors are usually unwilling to reveal their sensitive personal data and real identities to the data consumers. In this paper, we propose TPDM, which efficiently integrates data Truthfulness and Privacy preservation in Data Markets. TPDM is structured internally in an Encrypt-then-Sign fashion, using partially homomorphic encryption and identity-based signature. It simultaneously facilitates batch verification, data processing, and outcome verification, while maintaining identity preservation and data confidentiality. We also instantiate TPDM with a profile matching service and a distribution fitting service, and extensively evaluate their performances on Yahoo! Music ratings dataset and 2009 RECS dataset, respectively. Our analysis and evaluation results reveal that TPDM achieves several desirable properties, while incurring low computation and communication overheads when supporting large-scale data markets.
@cite_107: 
@cite_60: Data is a commodity. Recent research has considered the mathematical problem of setting prices for different queries over data. Ideal pricing functions need to be flexible --defined for arbitrary queries (select-project-join, aggregate, random sample, and noisy privacy-preserving queries). They should be fine-grained -- a consumer should not be required to buy the entire database to get answers to simple "low-information" queries (such as selecting only a few tuples or aggregating over only one attribute). Similarly, a consumer may not want to pay a large amount of money, only to discover that the database is empty. Finally, pricing functions should satisfy consistency conditions such as being "arbitrage-free" -- consumers should not be able to circumvent the pricing function by deducing the answer to an expensive query from a few cheap queries. Previously proposed pricing functions satisfy some of these criteria (i.e. they are defined for restricted subclasses of queries and or use relaxed conditions for avoiding arbitrage). In this paper, we study arbitrage-free pricing functions defined for arbitrary queries. We propose new necessary conditions for avoiding arbitrage and provide new arbitrage-free pricing functions. We also prove several negative results related to the tension between flexible pricing and avoiding arbitrage, and show how this tension often results in unreasonable prices.
@cite_120: Data has value and is increasingly being exchanged for commercial and research purposes. Data, however, is typically accompanied by terms of use, which limit how it can be used. To date, there are only a few, ad-hoc methods to enforce these terms. We propose DataLawyer, a new system to formally specify usage policies and check them automatically at query runtime in a relational database management system (DBMS). We develop a new model to specify policies compactly and precisely. We introduce novel algorithms to efficiently evaluate policies that can cut policy-checking overheads to only a few percent of the total query runtime. We implement DataLawyer and evaluate it on a real database from the health-care domain.
@cite_88: We propose AccountTrade, a set of accountable protocols, for big data trading among dishonest consumers. To secure the big data trading environment, our protocols achieve book-keeping ability and accountability against dishonest consumers who may misbehave throughout the dataset transactions. Specifically, we study the responsibilities of the consumers in the dataset trading and design AccountTrade to achieve accountability against the dishonest consumers who may try to deviate from their responsibilities. Specifically, we propose uniqueness index, a new rigorous measurement of the data uniqueness, as well as several accountable trading protocols to enable data brokers to blame the dishonest consumer when misbehavior is detected. We formally define, prove, and evaluate the accountability of our protocols by an automatic verification tool as well as extensive evaluation in real-world datasets. Our evaluation shows that AccountTrade incurs negligible constant storage overhead per file (<10KB), and it is able to handle 8–1000 concurrent data uploading per server depending on the data types.
@cite_47: When the analysis of individuals' personal information has value to an institution, but it compromises privacy, should individuals be compensated? We describe the foundations of a market in which those seeking access to data must pay for it and individuals are compensated for the loss of privacy they may suffer.
@cite_73: Data is increasingly being bought and sold online, and Web-based marketplace services have emerged to facilitate these activities. However, current mechanisms for pricing data are very simple: buyers can choose only from a set of explicit views, each with a specific price. In this paper, we propose a framework for pricing data on the Internet that, given the price of a few views, allows the price of any query to be derived automatically. We call this capability "query-based pricing." We first identify two important properties that the pricing function must satisfy, called arbitrage-free and discount-free. Then, we prove that there exists a unique function that satisfies these properties and extends the seller's explicit prices to all queries. When both the views and the query are Unions of Conjunctive Queries, the complexity of computing the price is high. To ensure tractability, we restrict the explicit prices to be defined only on selection views (which is the common practice today). We give an algorithm with polynomial time data complexity for computing the price of any chain query by reducing the problem to network flow. Furthermore, we completely characterize the class of Conjunctive Queries without self-joins that have PTIME data complexity (this class is slightly larger than chain queries), and prove that pricing all other queries is NP-complete, thus establishing a dichotomy on the complexity of the pricing problem when all views are selection queries.
In recent years, data market design has gained increasing interest from the database community. The seminal paper @cite_107 by Balazinska discusses the implications of the emerging digital data markets, and lists the research opportunities in this direction. Koutris @cite_73 presented a flexible data trading format, , query-based data pricing. Later, Lin and Kifer @cite_60 designed an arbitrage-free pricing function for arbitrary query formats. For personal data sharing, Li @cite_47 proposed a theory of pricing private data based on differential privacy. Upadhyaya @cite_120 developed a middleware system, called DataLawyer, to formally specify data use policies, and to automatically enforce these pre-defined terms during data usage. Jung @cite_88 focused on the dataset resale issue at the dishonest data consumers.