Generalizing data to provide anonymity when disclosing information (abstract)
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
On the complexity of optimal K-anonymity
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Approximate algorithms for K-anonymity
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
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
Anonymization of set-valued data via top-down, local generalization
Proceedings of the VLDB Endowment
k-automorphism: a general framework for privacy preserving network publication
Proceedings of the VLDB Endowment
K-isomorphism: privacy preserving network publication against structural attacks
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
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The increasing popularity of social networks has generated tremendous amount of data to be exploited for commercial, research and many other valuable applications. However, the release of these data has raised an issue that personal privacy may be breached. Current practices of simply removing all identifiable personal information (such as names and social security numbers) before releasing the data is insufficient. More effective anonymization techniques are required. In this work, we propose a k-anonymization-based technique on set-valued network node data. The proposed algorithm is based on the principle of minimizing the number of addition and deletion operations to achieve k-anonymity. Numerical experiments on real dataset show that it requires less number of operations than current suppression-based approach.