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
k-automorphism: a general framework for privacy preserving network publication
Proceedings of the VLDB Endowment
k-symmetry model for identity anonymization in social networks
Proceedings of the 13th International Conference on Extending Database Technology
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
K-isomorphism: privacy preserving network publication against structural attacks
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Personalized privacy protection in social networks
Proceedings of the VLDB Endowment
Anonymizing Graphs Against Weight-Based Attacks
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
Privacy Preservation by k-Anonymization of Weighted Social Networks
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
STK-anonymity: k-anonymity of social networks containing both structural and textual information
Proceedings of the ACM SIGMOD Workshop on Databases and Social Networks
| Hi-index | 0.00 |
The increasing popularity of social networks, such as online communities and telecommunication systems, has generated interesting knowledge discovery and data mining problems. Since social networks usually contain personal information of individuals, preserving privacy in the release of social network data becomes an important concern. An adversary can use many types of background knowledge to conduct an attack, such as topological structure and/or basic graph properties. Unfortunately, most of the previous studies on privacy preservation can deal with simple graphs only, and cannot be applied to weighted graphs. Since there exists numerous unique weight-based information in weighted graphs that can be used to attack a victim's privacy, to resist such weightbased re-identification attacks becomes a great challenge. In this paper, we investigate the identity disclosure problem in weighted graphs. We propose k-possible anonymity to protect against weight-based attacks and develop a generalization based anonymization approach (named GA) to achieve k-possible anonymity for a weighted graph. Extensive experiments on real datasets show that the algorithm performs well in terms of protection it provides, and properties of the original weighed network can be recovered with relatively little bias through aggregation on a small number of sampled graphs.