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\(\delta \)-privacy: Bounding Privacy Leaks in Privacy Preserving Data Mining

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Data Privacy Management, Cryptocurrencies and Blockchain Technology (DPM 2017, CBT 2017)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 10436))

Abstract

We propose a new definition for privacy, called \(\delta \)-privacy, for privacy preserving data mining. The intuition of this work is, after obtaining a result from a data mining method, an adversary has better ability in discovering data providers’ privacy; if this improvement is large, the method, which generated the response, is not privacy considerate. \(\delta \)-privacy requires that no adversary could improve more than \(\delta \). This definition can be used to assess the risk of privacy leak in any data mining methods, in particular, we show its relations to differential privacy and data anonymity, the two major evaluation methods. We also provide a quantitative analysis on the tradeoff between privacy and utility, rigorously prove that the information gains of any \(\delta \)-private methods do not exceed \(\delta \). Under the framework of \(\delta \)-privacy, it is able to design a pricing mechanism for privacy-utility trading system, which is one of our major future works.

This work was done while this author was studying in The Ohio State University.

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Notes

  1. 1.

    If f is a one-way function, then given f(x), it is hard to compute an \(x'\) such that \(f(x') = f(x)\). But by definition of \(\mathsf {pub}\), given \(\mathsf {pub}(r)\), it is not hard to find a \(r'\in \mathcal {F}\) such that \(\mathsf {pub}(r') = \mathsf {pub}(r)\), therefore, \(\mathsf {pub}\) is not a one-way function. See Chap. 6 of [5] or Chap. 6 of [12] for rigorous definition of one-way functions and hardcore predicates.

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Authors and Affiliations

  1. The Voleon Group, Berkeley, USA

    Zhizhou Li

  2. The Ohio State University, Columbus, USA

    Ten H. Lai

Authors
  1. Zhizhou Li
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  2. Ten H. Lai
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Correspondence to Zhizhou Li .

Editor information

Editors and Affiliations

  1. Télécom SudParis, Evry, France

    Joaquin Garcia-Alfaro

  2. Department of Information and Communications Engineering, Autonomous University of Barcelona, Bellaterra, Spain

    Guillermo Navarro-Arribas

  3. Karlsruhe Institute of Technology, Karlsruhe, Germany

    Hannes Hartenstein

  4. Autonomous University of Barcelona, Bellaterra, Spain

    Jordi Herrera-Joancomartí

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Li, Z., Lai, T.H. (2017). \(\delta \)-privacy: Bounding Privacy Leaks in Privacy Preserving Data Mining. In: Garcia-Alfaro, J., Navarro-Arribas, G., Hartenstein, H., Herrera-Joancomartí, J. (eds) Data Privacy Management, Cryptocurrencies and Blockchain Technology. DPM CBT 2017 2017. Lecture Notes in Computer Science(), vol 10436. Springer, Cham. https://doi.org/10.1007/978-3-319-67816-0_8

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  • DOI: https://doi.org/10.1007/978-3-319-67816-0_8

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