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.
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.
@cite_107 @cite_60 @cite_120 @cite_88 @cite_47 @cite_73
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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. 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. Increasingly data is being bought and sold online. To facilitate such transactions, online data market-places have emerged to provide a service for sellers to price views on their data, and buyers to buy such views. These marketplaces neither support the sale of ad-hoc queries (that are not one of the specified views), nor do they support queries that join datasets. We present QueryMarket, a prototype data marketplace that automatically extrapolates prices to ad-hoc queries, including those with joins, from the manually priced views. We call this capability "query-based pricing" and describe how it is superior to existing pricing methods, and how it provides more flexible pricing for the sellers. We then show how QueryMarket implements query-based pricing and how it generates explanations for the prices it computes. In a data marketplace, producers of data provide query answers to consumers in exchange for payment. A market for data allows capital to flow from data consumers to data producers. This helps to finance the costs of data collection and dissemination, thereby increasing the availability of data throughout society. While nascent data markets exist on the Web, they are currently limited in the pricing mechanisms and interactions they support. In this paper we propose new criteria for interactive pricing in a data market: price functions should be non-disclosive, arbitrage-free, and regret-free. We study the structure of price functions meeting these criteria, and distinguish between NP-complete and PTIME cases for computing a conforming price function.