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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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, p. 14, 2010.
R. Gibbons, A primer in game theory. Harvester Wheatsheaf Hertfordshire, 1992.
R. K. Adl, M. Askari, K. Barker, and R. Safavi-Naini, “Privacy consensus in anonymization systems via game theory,” in Data and Applications Security and Privacy XXVI. Springer, 2012, pp. 74–89.
L. Xu, C. Jiang, J. Wang, Y. Ren, J. Yuan, and M. Guizani, “Game theoretic data privacy preservation: Equilibrium and pricing,” in 2015 IEEE International Conference on Communications (ICC), June 2015, pp. 7071–7076.
K. Barker, J. Denzinger, and R. Karimi Adl, “A negotiation game: Establishing stable privacy policies for aggregate reasoning,” University of Calgary, Technical Report, 2012. [Online]. Available: http://hdl.handle.net/1880/49282
H. Kargupta, K. Das, and K. Liu, “Multi-party, privacy-preserving distributed data mining using a game theoretic framework,” in Knowledge Discovery in Databases: PKDD 2007. Springer, 2007, pp. 523–531.
N. R. Nanavati and D. C. Jinwala, “A novel privacy preserving game theoretic repeated rational secret sharing scheme for distributed data mining,” dcj, vol. 91, p. 9426611777, 2013.
M. Halkidi and I. Koutsopoulos, “A game theoretic framework for data privacy preservation in recommender systems,” in Machine Learning and Knowledge Discovery in Databases. Springer, 2011, pp. 629–644.
L. Sweeney, “k-anonymity: A model for protecting privacy,” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 10, no. 05, pp. 557–570, 2002.
R. J. Bayardo and R. Agrawal, “Data privacy through optimal k-anonymization,” in Data Engineering, 2005. ICDE 2005. Proceedings. 21st International Conference on. IEEE, 2005, pp. 217–228.
V. S. Iyengar, “Transforming data to satisfy privacy constraints,” in Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2002, pp. 279–288.
A. Gionis and T. Tassa, “k-anonymization with minimal loss of information,” Knowledge and Data Engineering, IEEE Transactions on, vol. 21, no. 2, pp. 206–219, 2009.
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), 2012, pp. 708–717.
K. Bache and M. Lichman, “UCI machine learning repository,” 2013. [Online]. Available: http://archive.ics.uci.edu/ml
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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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