The proliferation of social networks in digital media has proved to be fruitful, but this rise in popularity is accompanied by user privacy concerns. Social network data has been published in various ways and preserving the privacy of individuals in the published data has become an important concern. Several algorithms have been developed for privacy preservation in relational data, but these algorithms cannot be applied directly to social networks as the nodes here have structural properties along with labels. In this paper, we propose an algorithm to achieve κ-anonymity and l-diversity in social network data which provides structural anonymity along with sensitive attribute protection. The proposed algorithm uses novel edge addition techniques which are also presented in this paper. We also propose a concept of partial anonymity to reduce anonymization cost for d>1. The empirical study shows that our algorithm requires significantly less number of edge additions for anonymization of social network data and has a substantially lower running time than the other algorithms previously proposed in the field. Copyright 2013 ACM.
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|Journal||Data powered by TypesetProceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining - ASONAM '13|
|Publisher||Data powered by TypesetACM Press|