The <Emphasis Type="Italic">k</Emphasis>-anonymity and <Emphasis Type="Italic">l</Emphasis>-diversity approaches for privacy preservation in social networks against neighborhood attacks |
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Authors: | Bin Zhou Jian Pei |
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Affiliation: | 1.School of Computing Science,Simon Fraser University,Burnaby,Canada |
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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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