Abstract
The privacy issues arising in big data applications can be dealt with an economical way. Privacy can be seen as a special type of goods, in a sense that it can be traded by the owner for incentives. In this chapter, we consider a private data collecting scenario where a data collector buys data from multiple data providers and employs anonymization techniques to protect data providers’ privacy. Anonymization causes a decline of data utility, therefore, the data provider can only sell his data at a lower price if his privacy is better protected. Achieving a balance between privacy protection and data utility is an important question for the data collector. Considering that different data providers treat privacy differently, and their privacy preferences are unknown to the collector, we propose a contract theoretic approach for data collector to deal with the data providers. By designing an optimal contract, the collector can make rational decisions on how to pay the data providers, and how to protect the providers’ privacy. Performance of the proposed contract is evaluated by numerical simulations and experiments on real-world data. The contract analysis shows that when the collector requires a large amount of data, he should ask data providers who care privacy less to provide as much as possible data. We also find that when the collector requires higher utility of data or the data become less profitable, the collector should provide a stronger protection of the providers’ privacy.
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Xu, L., Jiang, C., Qian, Y., Ren, Y. (2018). Contract-Based Private Data Collecting. In: Data Privacy Games. Springer, Cham. https://doi.org/10.1007/978-3-319-77965-2_3
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DOI: https://doi.org/10.1007/978-3-319-77965-2_3
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