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A Game Theoretic Framework for Analyzing Re-Identification Risk

Author

Listed:
  • Zhiyu Wan
  • Yevgeniy Vorobeychik
  • Weiyi Xia
  • Ellen Wright Clayton
  • Murat Kantarcioglu
  • Ranjit Ganta
  • Raymond Heatherly
  • Bradley A Malin

Abstract

Given the potential wealth of insights in personal data the big databases can provide, many organizations aim to share data while protecting privacy by sharing de-identified data, but are concerned because various demonstrations show such data can be re-identified. Yet these investigations focus on how attacks can be perpetrated, not the likelihood they will be realized. This paper introduces a game theoretic framework that enables a publisher to balance re-identification risk with the value of sharing data, leveraging a natural assumption that a recipient only attempts re-identification if its potential gains outweigh the costs. We apply the framework to a real case study, where the value of the data to the publisher is the actual grant funding dollar amounts from a national sponsor and the re-identification gain of the recipient is the fine paid to a regulator for violation of federal privacy rules. There are three notable findings: 1) it is possible to achieve zero risk, in that the recipient never gains from re-identification, while sharing almost as much data as the optimal solution that allows for a small amount of risk; 2) the zero-risk solution enables sharing much more data than a commonly invoked de-identification policy of the U.S. Health Insurance Portability and Accountability Act (HIPAA); and 3) a sensitivity analysis demonstrates these findings are robust to order-of-magnitude changes in player losses and gains. In combination, these findings provide support that such a framework can enable pragmatic policy decisions about de-identified data sharing.

Suggested Citation

  • Zhiyu Wan & Yevgeniy Vorobeychik & Weiyi Xia & Ellen Wright Clayton & Murat Kantarcioglu & Ranjit Ganta & Raymond Heatherly & Bradley A Malin, 2015. "A Game Theoretic Framework for Analyzing Re-Identification Risk," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-24, March.
  • Handle: RePEc:plo:pone00:0120592
    DOI: 10.1371/journal.pone.0120592
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    References listed on IDEAS

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    1. Zhu, Cheng-jie & Sun, Shi-wen & Wang, Li & Ding, Shuai & Wang, Juan & Xia, Cheng-yi, 2014. "Promotion of cooperation due to diversity of players in the spatial public goods game with increasing neighborhood size," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 406(C), pages 145-154.
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    Cited by:

    1. Alejandro Noriega-Campero & Alex Rutherford & Oren Lederman & Yves A. de Montjoye & Alex Pentland, 2018. "Mapping the Privacy-Utility Tradeoff in Mobile Phone Data for Development," Papers 1808.00160, arXiv.org.
    2. Cuquet, Martí & Fensel, Anna, 2018. "The societal impact of big data: A research roadmap for Europe," Technology in Society, Elsevier, vol. 54(C), pages 74-86.

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