Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
STOC '83 Proceedings of the fifteenth annual ACM symposium on Theory of computing
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
The union-split algorithm and cluster-based anonymization of social networks
Proceedings of the 4th International Symposium on Information, Computer, and Communications Security
Proceedings of the 3rd Workshop on Social Network Mining and Analysis
Anonymizing bipartite graph data using safe groupings
The VLDB Journal — The International Journal on Very Large Data Bases
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
Personalized privacy protection in social networks
Proceedings of the VLDB Endowment
Knowledge and Information Systems
Social Network Privacy for Attribute Disclosure Attacks
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
A refined complexity analysis of degree anonymization in graphs
ICALP'13 Proceedings of the 40th international conference on Automata, Languages, and Programming - Volume Part II
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In recent years, concerns of privacy have become more prominent for social networks. Anonymizing a graph meaningfully is a challenging problem, as the original graph properties must be preserved as well as possible. We introduce a generalization of the degree anonymization problem posed by Liu and Terzi. In this problem, our goal is to anonymize a given subset of nodes while adding the fewest possible number of edges. The main contribution of this paper is an efficient algorithm for this problem by exploring its connection with the degree-constrained sub graph problem. Our experimental results show that our algorithm performs very well on many instances of social network data.