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Providing k-anonymity in data mining

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Abstract

In this paper we present extended definitions of k-anonymity and use them to prove that a given data mining model does not violate the k-anonymity of the individuals represented in the learning examples. Our extension provides a tool that measures the amount of anonymity retained during data mining. We show that our model can be applied to various data mining problems, such as classification, association rule mining and clustering. We describe two data mining algorithms which exploit our extension to guarantee they will generate only k-anonymous output, and provide experimental results for one of them. Finally, we show that our method contributes new and efficient ways to anonymize data and preserve patterns during anonymization.

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Authors and Affiliations

  1. Computer Science Department, Technion—Israel Institute of Technology, Haifa, Israel

    Arik Friedman & Ran Wolff

  2. Management Information Systems Department, Haifa University, Haifa, Israel

    Assaf Schuster

Authors
  1. Arik Friedman
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  2. Ran Wolff
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  3. Assaf Schuster
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Correspondence to Arik Friedman.

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Friedman, A., Wolff, R. & Schuster, A. Providing k-anonymity in data mining. The VLDB Journal 17, 789–804 (2008). https://doi.org/10.1007/s00778-006-0039-5

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  • DOI: https://doi.org/10.1007/s00778-006-0039-5

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