A clustering approach for structural k-anonymity in social networks using genetic algorithm

Authors:
Vikas Kumar Sihag
Affiliations:
Motilal Nehru National Institute of Technology Allahabad, Allahabad, India
Venue:
Proceedings of the CUBE International Information Technology Conference
Year:
2012

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Abstract

With an abundance of social network data being released, the need to protect sensitive information within these networks has become an important concern of data publishers. One of privacy preserving approaches often used is anonymization. This paper focuses on the popular notion of k-anonymization, where k is the required threshold of structural anonymity. Given a social network graph, it transforms G to G', such that structural property of each node in G' is attained by at least k --- 1 other nodes in G'. The nodes are clustered together into supernodes of size at least k. The above being NP-hard optimization problem, a genetic algorithm is proposed to optimize it's structural k-anonymity. Edge generalization is then employed, based on their relationships to achieve indistinguishable nodes.