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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References
B. Fung, K. Wang, R. Chen, and P. S. Yu, “Privacy-preserving data publishing: A survey of recent developments,” ACM Comput. Surv., vol. 42, no. 4, pp. 1–53, 2010.
R. Agrawal and R. Srikant, “Privacy-preserving data mining,” SIGMOD Rec., vol. 29, no. 2, pp. 439–450, 2000.
L. SWEENEY, “Achieving k-anonymity privacy protection using generalization and suppression,” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 10, no. 05, pp. 571–588, 2002.
A. Machanavajjhala, J. Gehrke, D. Kifer, and M. Venkitasubramaniam, “L-diversity: privacy beyond k-anonymity,” in Data Engineering, 2006. ICDE ‘06. Proceedings of the 22nd International Conference on, April 2006, pp. 24–24.
N. Li, T. Li, and S. Venkatasubramanian, “t-closeness: Privacy beyond k-anonymity and l-diversity.” in ICDE, vol. 7, 2007, pp. 106–115.
C. C. Aggarwal and S. Y. Philip, A general survey of privacy-preserving data mining models and algorithms. Springer, 2008.
S. Matwin, “Privacy-preserving data mining techniques: Survey and challenges,” in Discrimination and Privacy in the Information Society. Springer, 2013, pp. 209–221.
A. Acquisti, C. R. Taylor, and L. Wagman, “The economics of privacy,” Journal of Economic Literature, vol. 52, no. 2, 2016.
A. Roth, “Buying private data at auction: the sensitive surveyor’s problem.” SIGecom Exchanges, vol. 11, no. 1, pp. 1–8, 2012.
A. Ghosh and A. Roth, “Selling privacy at auction,” in Proceedings of the 12th ACM conference on Electronic commerce. ACM, 2011, pp. 199–208.
L. K. Fleischer and Y.-H. Lyu, “Approximately optimal auctions for selling privacy when costs are correlated with data,” in Proceedings of the 13th ACM Conference on Electronic Commerce. ACM, 2012, pp. 568–585.
K. Ligett and A. Roth, “Take it or leave it: Running a survey when privacy comes at a cost,” in Internet and Network Economics. Springer, 2012, pp. 378–391.
K. Nissim, S. Vadhan, and D. Xiao, “Redrawing the boundaries on purchasing data from privacy-sensitive individuals,” in Proceedings of the 5th conference on Innovations in theoretical computer science. ACM, 2014, pp. 411–422.
C. Dwork, “Differential privacy,” in Automata, languages and programming. Springer, 2006, pp. 1–12.
J.-J. Laffont and D. Martimort, The theory of incentives: the principal-agent model. Princeton University Press, 2009.
L. Xu, C. Jiang, Y. Chen, Y. Ren, and K. J. R. Liu, “Privacy or utility in data collection? a contract theoretic approach,” IEEE Journal of Selected Topics in Signal Processing, vol. 9, no. 7, pp. 1256–1269, Oct 2015.
D. Kirk, Optimal Control Theory: An Introduction, ser. Dover Books on Electrical Engineering. Dover Publications, 2012.
K. Bache and M. Lichman, “UCI machine learning repository,” 2013. [Online]. Available: http://archive.ics.uci.edu/ml
K. LeFevre, D. J. DeWitt, and R. Ramakrishnan, “Incognito: Efficient full-domain k-anonymity,” in Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, ser. SIGMOD ‘05. New York, NY, USA: ACM, 2005, pp. 49–60. [Online]. Available: http://doi.acm.org/10.1145/1066157.1066164
F. Kohlmayer, F. Prasser, C. Eckert, A. Kemper, and K. Kuhn, “Flash: Efficient, stable and optimal k-anonymity,” in Privacy, Security, Risk and Trust (PASSAT), 2012 International Conference on and 2012 International Confernece on Social Computing (SocialCom), Sept 2012, pp. 708–717.
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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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