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The k-anonymity and l-diversity approaches for privacy preservation in social networks against neighborhood attacks

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Abstract

Recently, more and more social network data have been published in one way or another. Preserving privacy in publishing social network data becomes an important concern. With some local knowledge about individuals in a social network, an adversary may attack the privacy of some victims easily. Unfortunately, most of the previous studies on privacy preservation data publishing can deal with relational data only, and cannot be applied to social network data. In this paper, we take an initiative toward preserving privacy in social network data. Specifically, we identify an essential type of privacy attacks: neighborhood attacks. If an adversary has some knowledge about the neighbors of a target victim and the relationship among the neighbors, the victim may be re-identified from a social network even if the victim’s identity is preserved using the conventional anonymization techniques. To protect privacy against neighborhood attacks, we extend the conventional k-anonymity and l-diversity models from relational data to social network data. We show that the problems of computing optimal k-anonymous and l-diverse social networks are NP-hard. We develop practical solutions to the problems. The empirical study indicates that the anonymized social network data by our methods can still be used to answer aggregate network queries with high accuracy.

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Authors and Affiliations

  1. School of Computing Science, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada

    Bin Zhou & Jian Pei

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  1. Bin Zhou
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  2. Jian Pei
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Correspondence to Bin Zhou.

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A preliminary version of this paper appears as Zhou and Pei [49]. This research is supported in part by an NSERC Discovery Grant and an NSERC Discovery Accelerator Supplement Grant. All opinions, findings, conclusions and recommendations in this paper are those of the authors and do not necessarily reflect the views of the funding agencies.

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Zhou, B., Pei, J. The k-anonymity and l-diversity approaches for privacy preservation in social networks against neighborhood attacks. Knowl Inf Syst 28, 47–77 (2011). https://doi.org/10.1007/s10115-010-0311-2

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  • DOI: https://doi.org/10.1007/s10115-010-0311-2

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