Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
On k-anonymity and the curse of dimensionality
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Checking for k-anonymity violation by views
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Injecting utility into anonymized datasets
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Anonymizing sequential releases
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Utility-based anonymization using local recoding
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
M-invariance: towards privacy preserving re-publication of dynamic datasets
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Dynamic anonymization: accurate statistical analysis with privacy preservation
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
ANGEL: Enhancing the Utility of Generalization for Privacy Preserving Publication
IEEE Transactions on Knowledge and Data Engineering
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Marginal publication is one of important techniques to help researchers to improve the understanding about correlation between published attributes. However, without careful treatment, it's of high risk of privacy leakage for marginal publications. Solution like ANGEL has been available to eliminate such risks of privacy leakage. But, unfortunately, query accuracy has been paid as the cost for the privacy-safety of ANGEL. To improve the data utility of marginal publication while ensuring privacy-safety, we propose a new technique called dynamic anonymization. We present the detail of the technique and theoretical properties of the proposed approach. Extensive experiments on real data show that our technique allows highly effective data analysis, while offering strong privacy guarantees.