k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Anatomy: simple and effective privacy preservation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD)
Proceedings of the 16th international conference on World Wide Web
Minimality attack in privacy preserving data publishing
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
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
Anonymizing bipartite graph data using safe groupings
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
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
P-Sensitive K-Anonymity with Generalization Constraints
Transactions on Data Privacy
Prediction promotes privacy in dynamic social networks
WOSN'10 Proceedings of the 3rd conference on Online social networks
Resisting structural re-identification in anonymized social networks
The VLDB Journal — The International Journal on Very Large Data Bases
Knowledge and Information Systems
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With the popularity of social networks, the privacy issues related with social network data become more and more important. The connection information between users, as well as their sensitive attributes, should be protected. There are some proposals studying how to publish a privacy preserving graph. However, when the algorithm which generates the published graph is known by the attacker, the current protection models may still leak some connection information. In this paper, we propose a new protection model, "Semi-Edge Anonymity", to protect both user's sensitive attributes and connection information even when an attacker knows the publication algorithm. Moreover, any state-of-art tabular data protection techniques can be applied to Semi-Edge Anonymity model to protect sensitive attributes. We theoretically prove that on two utilities, the possible world size and the true edge ratio, the Semi-Edge Anonymity model outperforms any clustering based model which protects links. We further conduct extensive experiments on real data sets for two other utilities. The results show that our model also has better performance on these utilities than the clustering based models.