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Privacy-Preserving Data Mining pp 359–381Cite as

A Survey of Attack Techniques on Privacy-Preserving Data Perturbation Methods

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

We focus primarily on the use of additive and matrix multiplicative data perturbation techniques in privacy preserving data mining (PPDM). We survey a recent body of research aimed at better understanding the vulnerabilities of these techniques. These researchers assumed the role of an attacker and developed methods for estimating the original data from the perturbed data and any available prior knowledge. Finally, we briefly discuss research aimed at attacking k-anonymization, another data perturbation technique in PPDM.

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Author information

Authors and Affiliations

  1. IBM Almaden esearch Center, 95120, San Jose, CA, USA

    Kun Liu

  2. Department of Computer Science, Loyola College in Maryland, 21210, Baltimore, MD, USA

    Chris Giannella

  3. Department of Computer Science and Electrical Engineering, University of Maryland, 21250, Baltimore, MD, USA

    Hillol Kargupta

Authors
  1. Kun Liu
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  2. Chris Giannella
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  3. Hillol Kargupta
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Editor information

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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Liu, K., Giannella, C., Kargupta, H. (2008). A Survey of Attack Techniques on Privacy-Preserving Data Perturbation Methods. 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_15

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  • DOI: https://doi.org/10.1007/978-0-387-70992-5_15

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-70991-8

  • Online ISBN: 978-0-387-70992-5

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