Security of random data perturbation methods
ACM Transactions on Database Systems (TODS)
Practical Data-Oriented Microaggregation for Statistical Disclosure Control
IEEE Transactions on Knowledge and Data Engineering
Sensitive Micro Data Protection Using Latin Hypercube Sampling Technique
Inference Control in Statistical Databases, From Theory to Practice
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Transforming data to satisfy privacy constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-preserving k-means clustering over vertically partitioned data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Privacy through Optimal k-Anonymization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
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Privacy preservation is an important and challenging problem in microdata release. As a de-identification model, k-anonymity has gained much attention recently. While focusing on identity disclosures, k-anonymity does not well resolve attribute disclosures. In this paper we focus on the sensitive attribute disclosures in k-anonymity and propose an ordinal distance based sensitivity aware diversity metric. We assume the more diversity the sensitive attribute assumes in an equivalence class in a k-anonymized table, the less inference channel there is in the equivalence class.