The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Proceedings of the 16th international conference on World Wide Web
Towards identity anonymization on graphs
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Resisting structural re-identification in anonymized social networks
Proceedings of the VLDB Endowment
Preserving Privacy in Social Networks Against Neighborhood Attacks
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Preserving the privacy of sensitive relationships in graph data
PinKDD'07 Proceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD
Social Network Analysis and Mining for Business Applications
ACM Transactions on Intelligent Systems and Technology (TIST)
Privacy-aware data management in information networks
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
All liaisons are dangerous when all your friends are known to us
Proceedings of the 22nd ACM conference on Hypertext and hypermedia
Limiting link disclosure in social network analysis through subgraph-wise perturbation
Proceedings of the 15th International Conference on Extending Database Technology
Sensitive label privacy protection on social network data
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
A clustering approach for structural k-anonymity in social networks using genetic algorithm
Proceedings of the CUBE International Information Technology Conference
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Many applications of social networks require relationship anonymity due to the sensitive, stigmatizing, or confidential nature of relationship. Recent work showed that the simple technique of anonymizing graphs by replacing the identifying information of the nodes with random ids does not guarantee privacy since the identification of the nodes can be seriously jeopardized by applying subgraph queries. In this paper, we investigate how well an edge based graph randomization approach can protect sensitive links. We show via theoretical studies and empirical evaluations that various similarity measures can be exploited by attackers to significantly improve their confidence and accuracy of predicted sensitive links between nodes with high similarity values.