Abstract
Collecting and publishing personal data may lead to the disclosure of individual privacy. In this chapter, we consider a scenario where a data collector collects data from data providers and then publish the data to a data miner. To protect data providers’ privacy, the data collector performs anonymization on the data. Anonymization usually causes a decline of data utility on which the data miner’s profit depends, meanwhile, data providers would provide more data if anonymity is strongly guaranteed. How to make a trade-off between privacy protection and data utility is an important question for data collector. We model the interactions among data providers, data collector and data miner as a game. A backward induction-based approach is proposed to find the Nash equilibria of the game. To elaborate the analysis, we also present a specific game formulation which uses k-anonymity as the privacy model. Simulation results show that the game theoretic analysis can help the data collector to achieve a better trade-off between privacy and utility.
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Xu, L., Jiang, C., Qian, Y., Ren, Y. (2018). Privacy-Preserving Data Collecting: A Simple Game Theoretic Approach. In: Data Privacy Games. Springer, Cham. https://doi.org/10.1007/978-3-319-77965-2_2
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DOI: https://doi.org/10.1007/978-3-319-77965-2_2
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