Game theoretic data privacy preservation: Equilibrium and pricing

Privacy issues arising in the process of collecting, publishing and mining individuals' personal data have attracted much attention in recent years. In this paper, we consider a scenario where a data collector collects data from data providers and then publish the data to a data user. 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 user'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. In this paper we model the interactions among data providers/collector/user as a game, and propose a general approach to find the Nash equilibriums of the game. To elaborate the analysis, we also present a specific game formulation which takes k-anonymity as the anonymization method. Simulation results show that the game theoretical analysis can help the data collector to deal with the privacy-utility trade-off.

[1]  A. Asuncion,et al.  UCI Machine Learning Repository, University of California, Irvine, School of Information and Computer Sciences , 2007 .

[2]  Vijay S. Iyengar,et al.  Transforming data to satisfy privacy constraints , 2002, KDD.

[3]  Kun Liu,et al.  Multi-party, Privacy-Preserving Distributed Data Mining Using a Game Theoretic Framework , 2007, PKDD.

[4]  Claudia Eckert,et al.  Flash: Efficient, Stable and Optimal K-Anonymity , 2012, 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing.

[5]  Latanya Sweeney,et al.  k-Anonymity: A Model for Protecting Privacy , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[6]  Ken Barker,et al.  A Negotiation Game: Establishing Stable Privacy Policies for Aggregate Reasoning , 2012 .

[7]  Reihaneh Safavi-Naini,et al.  Privacy Consensus in Anonymization Systems via Game Theory , 2012, DBSec.

[8]  Philip S. Yu,et al.  Privacy-preserving data publishing: A survey of recent developments , 2010, CSUR.

[9]  Iordanis Koutsopoulos,et al.  A Game Theoretic Framework for Data Privacy Preservation in Recommender Systems , 2011, ECML/PKDD.

[10]  Devesh C. Jinwala,et al.  A game theory based repeated rational secret sharing scheme for privacy preserving distributed data mining , 2013, 2013 International Conference on Security and Cryptography (SECRYPT).

[11]  Roberto J. Bayardo,et al.  Data privacy through optimal k-anonymization , 2005, 21st International Conference on Data Engineering (ICDE'05).

[12]  Robert Gibbons,et al.  A primer in game theory , 1992 .

[13]  Tamir Tassa,et al.  k -Anonymization with Minimal Loss of Information , 2007, ESA.