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Incentive Compatible Privacy-Preserving Distributed Classification
July-Aug. 2012 (vol. 9 no. 4)
pp. 451-462
Robert Nix, University of Texas at Dallas, Richardson
Murat Kantarcioglu, University of Texas at Dallas, Richardson
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.

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Index Terms:
game theory, data mining, privacy, mechanism design.
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
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