The Stanford GraphBase: a platform for combinatorial computing
The Stanford GraphBase: a platform for combinatorial computing
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Proceedings of the 16th international conference on World Wide Web
Resisting structural re-identification in anonymized social networks
Proceedings of the VLDB Endowment
k-automorphism: a general framework for privacy preserving network publication
Proceedings of the VLDB Endowment
Anonymizing Graphs Against Weight-Based Attacks
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
On Identity Disclosure in Weighted Graphs
PDCAT '10 Proceedings of the 2010 International Conference on Parallel and Distributed Computing, Applications and Technologies
Anonymizing shortest paths on social network graphs
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
Compression of weighted graphs
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
A generalization based approach for anonymizing weighted social network graphs
WAIM'11 Proceedings of the 12th international conference on Web-age information management
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Privacy preserving analysis of a social network aims at a better understanding of the network and its behavior, while at the same time protecting the privacy of its individuals. We propose an anonymization method for weighted graphs, i.e., for social networks where the strengths of links are important. This is in contrast with many previous studies which only consider unweighted graphs. Weights can be essential for social network analysis, but they pose new challenges to privacy preserving network analysis. In this paper, we mainly consider prevention of identity disclosure, but we also touch on edge and edge weight disclosure in weighted graphs. We propose a method that provides k-anonymity of nodes against attacks where the adversary has information about the structure of the network, including its edge weights. The method is efficient, and it has been evaluated in terms of privacy and utility on real word datasets.