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Privacy-Preserving Data Mining pp 81–103Cite as

Measures of Anonymity

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Part of the book series: Advances in Database Systems ((ADBS,volume 34))

To design a privacy-preserving data publishing system, we must first quantify the very notion of privacy, or information loss. In the past few years, there has been a proliferation of measures of privacy, some based on statistical considerations, others based on Bayesian or information-theoretic notions of information, and even others designed around the limitations of bounded adversaries. In this chapter, we review the various approaches to capturing privacy. We will find that although one can define privacy from different standpoints, there are many structural similarities in the way different approaches have evolved. It will also become clear that the notions of privacy and utility (the useful information one can extract from published data) are intertwined in ways that are yet to be fully resolved.

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Author information

Authors and Affiliations

  1. School of Computing, University of Utah, McLean, UT, USA

    Suresh Venkatasubramanian

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  1. Suresh Venkatasubramanian
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Editors and Affiliations

  1. IBM Thomas J. Watson Research Center, 19 Skyline Drive, 10532, Hawthorne, NY, USA

    Charu C. Aggarwal

  2. Department of Computer Science, University of Illinois at Chicago, 854 South Morgan Street, 60607-7053, Chicago, IL, USA

    Philip S. Yu

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Venkatasubramanian, S. (2008). Measures of Anonymity. In: Aggarwal, C.C., Yu, P.S. (eds) Privacy-Preserving Data Mining. Advances in Database Systems, vol 34. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-70992-5_4

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  • DOI: https://doi.org/10.1007/978-0-387-70992-5_4

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-70991-8

  • Online ISBN: 978-0-387-70992-5

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