Sensitive edges protection in social networks

Authors:
Liangwen Yu;Tao Yang;Zhengang Wu;Jiawei Zhu;Jianbin Hu;Zhong Chen
Affiliations:
Institute of Software, School of EECS, Peking University, China,MoE Key Lab of High Confidence Software Technologies, PKU, China,MoE Key Lab of Network and Software Security Assurance, PKU, China;Institute of Software, School of EECS, Peking University, China,MoE Key Lab of High Confidence Software Technologies, PKU, China,MoE Key Lab of Network and Software Security Assurance, PKU, China;Institute of Software, School of EECS, Peking University, China,MoE Key Lab of High Confidence Software Technologies, PKU, China,MoE Key Lab of Network and Software Security Assurance, PKU, China;Institute of Software, School of EECS, Peking University, China,MoE Key Lab of High Confidence Software Technologies, PKU, China,MoE Key Lab of Network and Software Security Assurance, PKU, China;Institute of Software, School of EECS, Peking University, China,MoE Key Lab of High Confidence Software Technologies, PKU, China,MoE Key Lab of Network and Software Security Assurance, PKU, China;Institute of Software, School of EECS, Peking University, China,MoE Key Lab of High Confidence Software Technologies, PKU, China,MoE Key Lab of Network and Software Security Assurance, PKU, China
Venue:
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
Year:
2013

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Abstract

With the increasing popularity of online social networks, such as twitter and weibo, privacy preserving publishing of social network data has raised serious concerns. In this paper, we focus on the problem of preserving the sensitive edges in social network data. We call a graph is k-sensitive anonymous if the probability of an attacker can re-identify a sensitive node or a sensitive edge is at most $\frac{1}{k}$. To achieve this objective, we devise two efficient heuristic algorithms to respectively group sensitive nodes and create non-sensitive edges. Finally, we verify the effectiveness of the algorithm through experiments.