Answering vertex aggregate queries using anonymized social network data

Authors:
Bin Zhou
Affiliations:
University of Maryland, Baltimore County
Venue:
Proceedings of the 1st Workshop on Privacy and Security in Online Social Media
Year:
2012

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Knowledge and Information Systems

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Abstract

As social network data contain rich information about individuals, privacy becomes a critical concern in publishing and exchanging social network data. The existing approaches for privacy-preserving social network data publishing have to modify the local structures of a network substantially which may lead to considerable loss in answering some vertex aggregate queries (e.g., analyzing the degree distribution of vertices). In this paper, we propose a graph partitioning framework to anonymize social network data against degree attacks. A distinct advantage of our approach is that the anonymized social network data can be used to answer some vertex aggregate queries accurately. An empirical study using a large real dataset clearly verifies the effectiveness of our approach.