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Issue No.04 - July-Aug. (2012 vol.9)
pp: 451-462
Robert Nix , University of Texas at Dallas, Richardson
Murat Kantarcioglu , University of Texas at Dallas, Richardson
ABSTRACT
In this paper, we propose game-theoretic mechanisms to encourage truthful data sharing for distributed data mining. One proposed mechanism uses the classic Vickrey-Clarke-Groves (VCG) mechanism, and the other relies on the Shapley value. Neither relies on the ability to verify the data of the parties participating in the distributed data mining protocol. Instead, we incentivize truth telling based solely on the data mining result. This is especially useful for situations where privacy concerns prevent verification of the data. Under reasonable assumptions, we prove that these mechanisms are incentive compatible for distributed data mining. In addition, through extensive experimentation, we show that they are applicable in practice.
INDEX TERMS
game theory, data mining, privacy, mechanism design.
CITATION
Robert Nix, Murat Kantarcioglu, "Incentive Compatible Privacy-Preserving Distributed Classification", IEEE Transactions on Dependable and Secure Computing, vol.9, no. 4, pp. 451-462, July-Aug. 2012, doi:10.1109/TDSC.2011.52
REFERENCES
[1] I. Abraham, D. Dolev, R. Gonen, and J. Halpern, “Distributed Computing Meets Game Theory: Robust Mechanisms for Rational Secret Sharing and Multiparty Computation,” Proc. 25th Ann. ACM Symp. Principles of Distributed Computing, pp. 53-62, ACM, 2006.
[2] R. Agrawal and E. Terzi, “On Honesty in Sovereign Information Sharing,” Lecture Notes in Computer Science, Springer, vol. 3896, p. 240, 2006.
[3] G. Annas, “HIPAA Regulations-A New Era of Medical-Record Privacy?,” The New England J. Medicine, vol. 348, no. 15, p. 1486, 2003.
[4] I. Ashlagi, A. Klinger, and M. Tenneholtz, “K-NCC: Stability Against Group Deviations in Non-Cooperative Computation,” Lecture Notes in Computer Science, Springer, vol. 4858, p. 564, 2007.
[5] A. Asuncion and D. Newman, “UCI Machine Learning Repository,” 2007.
[6] R. Aumann, “The Core of a Cooperative Game Without Side Payments,” Trans. Am. Math. Soc., vol. 98, no. 3, pp. 539-552, 1961.
[7] C. Clifton, M. Kantarcioglu, J. Vaidya, X. Lin, and M. Zhu, “Tools for Privacy Preserving Distributed Data Mining,” ACM SIGKDD Explorations Newsletter, vol. 4, no. 2, pp. 28-34, 2002.
[8] O. Dekel, F. Fischer, and A. Procaccia, “Incentive Compatible Regression Learning,” Proc. 19th Ann. ACM-SIAM Symp. Discrete Algorithms, pp. 884-893, Soc. Ind'l. Appl. Math, 2008.
[9] C. Dwork, K. Kenthapadi, F. McSherry, I. Mironov, and M. Naor, “Our Data, Ourselves: Privacy Via Distributed Noise Generation,” Advances in Cryptology-EUROCRYPT 2006, pp. 486-503, 2006.
[10] S. Fatima, M. Wooldridge, and N. Jennings, “A Linear Approximation Method for the Shapley Value,” Artificial Intelligence, vol. 172, no. 14, pp. 1673-1699, 2008.
[11] N. Garg and D. Grosu, “A Faithful Distributed Mechanism for Sharing the Cost of Multicast Transmissions,” IEEE Trans. Parallel and Distributed Systems, pp. 1089-1101, 2008.
[12] S. Gordon and J. Katz, “Rational Secret Sharing, Revisited,” Lecture Notes in Computer Science, Springer, vol. 4116, p. 229, 2006.
[13] M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. Witten, “The WEKA Data Mining Software: An Update.” ACM SIGKDD Explorations Newsletter vol. 11, no. 1, June 2009.
[14] J. Halpern and V. Teague, “Rational Secret Sharing and Multiparty Computation: Extended Abstract,” Proc. 36th Ann. ACM Symp. Theory of Computing, pp. 623-632, ACM New York, 2004.
[15] M. Islam and L. Brankovic, “Noise Addition for Protecting Privacy in Data Mining,” Proc. Sixth Eng. Math. Applications Conf. (EMAC '03), pp. 85-90, 2003.
[16] S. Izmalkov, S. Micali, and M. Lepinski, “Rational Secure Computation and Ideal Mechanism Design,” Proc. 46th Ann. IEEE Symp. Foundations of Computer Science, 2005, (FOCS '05), pp. 585-594, 2005.
[17] W. Jiang, C. Clifton, and M. Kantarcioğlu, “Transforming Semi-Honest Protocols to Ensure Accountability,” Data & Knowledge Eng., vol. 65, no. 1, pp. 57-74, 2008.
[18] M. Kantarcioglu and J. Vaidya, “Privacy Preserving Naive Bayes Classifier for Horizontally Partitioned Data,” 2003.
[19] H. Kargupta, K. Das, and K. Liu, “Multi-Party, Privacy-Preserving Distributed Data Mining Using a Game Theoretic Framework,” Knowledge Discovery in Databases: PKDD 2007, pp. 523-531, 2007.
[20] J. Katz, “Bridging Game Theory and Cryptography: Recent Results and Future Directions,” Lecture Notes in Computer Science, Springer, vol. 4948, p. 251, 2008.
[21] G. Kol and M. Naor, “Cryptography and Game Theory: Designing Protocols for Exchanging Information,” Lecture Notes in Computer Science. Springer, vol. 4948, p. 320, 2008.
[22] R. Layfield, M. Kantarcioglu, and B. Thuraisingham, “Incentive and Trust Issues in Assured Information Sharing,” Proc. Fourth Int'l Conf. Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom '08), Nov. 13-16, 2008.
[23] S. Littlechild and G. Owen, “A Simple Expression for the Shapely Value in a Special Case,” Management Science, vol. 20, no. 3, pp. 370-372, 1973.
[24] A. Lysyanskaya and N. Triandopoulos, “Rationality and Adversarial Behavior in Multi-Party Computation,” Lecture Notes in Computer Science, Springer, vol. 4117, pp. 180-197, 2006.
[25] A. Machanavajjhala, D. Kifer, J. Gehrke, and M. Venkitasubramaniam, “l-diversity: Privacy Beyond k-Anonymity,” ACM Trans. Knowledge Discovery from Data (TKDD), vol. 1, no. 1, p. 3, 2007.
[26] R. McGrew, R. Porter, and Y. Shoham, “Towards a General Theory of Non-Cooperative Computation,” Proc. Ninth Conf. Theoretical Aspects of Rationality and Knowledge, pp. 59-71, ACM New York, 2003.
[27] H. Moulin, “An Application of the Shapley Value to Fair Division with Money,” Econometrica: J. Econometric Society, vol. 60, no. 6, pp. 1331-1349, 1992.
[28] N. Nisan, “Introduction to Mechanism Design (for Computer Scientists),” Algorithmic Game Theory, pp. 209-242, 2007.
[29] S. Ong, D. Parkes, A. Rosen, and S. Vadhan, “Fairness with an Honest Minority and a Rational Majority,” Proc. Sixth Theory of Cryptography Conf. (TCC), Springer, 2009.
[30] D. Parkes and J. Shneidman, “Distributed Implementations of Vickrey-Clarke-Groves Mechanisms,” Proc. Third IEEE Int'l Joint Conf. Autonomous Agents and Multiagent Systems-Volume 1, pp. 261-268, IEEE Computer Society, 2004.
[31] B. Pinkas, “Cryptographic Techniques for Privacy-Preserving Data Mining,” ACM SIGKDD Explorations Newsletter, vol. 4, no. 2, p. 19, 2002.
[32] E. Rasmusen, Games and Information: An Introduction to Game Theory. Blackwell Pub, 2007.
[33] L. Shapley, A Value for n-Person Games Cambridge Univ. Press, 1952.
[34] Y. Shoham and M. Tennenholtz, “Non-cooperative Computation: Boolean Functions with Correctness and Exclusivity,” Theoretical Computer Science, vol. 343, nos. 1-2, pp. 97-113, 2005.
[35] L. Sweeney, “k-Anonymity: A Model For Protecting Privacy,” World, vol. 10, no. 5, pp. 557-570, 2002.
[36] X. Tan and T. Lie, “Application of the Shapley Value on Transmission Cost Allocation in The Competitive Power Market Environment,” Proc. IEE Generation, Transmission and Distribution, vol. 149, pp. 15-20, IET, 2002.
[37] J. Von Neumann and O. Morgenstern, Theory of Games and Economic Behavior. John Wiley & Sons Inc, 1967.
[38] X. Xiao and Y. Tao, “M-invariance: Towards Privacy Preserving Re-Publication of Dynamic Data sets,” Proc. 2007 ACM SIGMOD Int'l Conf. Management of Data, pp. 689-700, ACM, 2007.
[39] N. Zhang and W. Zhao, “Distributed privacy preserving information sharing,” Proc. 31st Int'l Conf. Very Large Data Bases, VLDB '05, pp. 889-900, VLDB Endowment, 2005.
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