Data mining: concepts and techniques
Data mining: concepts and techniques
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
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
Top-Down Specialization for Information and Privacy Preservation
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Data Privacy through Optimal k-Anonymization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
On the complexity of optimal K-anonymity
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Incognito: efficient full-domain K-anonymity
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Checking for k-anonymity violation by views
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Disclosure risk measures for microdata
SSDBM '03 Proceedings of the 15th International Conference on Scientific and Statistical Database Management
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Injecting utility into anonymized datasets
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Towards a more reasonable generalization cost metric for k-anonymization
BNCOD'06 Proceedings of the 23rd British National Conference on Databases, conference on Flexible and Efficient Information Handling
Privacy inference attacking and prevention on multiple relative k-anonymized microdata sets
APWeb'08 Proceedings of the 10th Asia-Pacific web conference on Progress in WWW research and development
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General k-anonymity models cannot fully prevent individual re-identification on released microdata since intruders can capture various prior information to recognized individual identities with enough precision, ultimately, to precisely associate these identities with some sensitive attribute values. We propose the privacy inference logic to specify the k-anonymity method and emphasize the nature of privacy inference attacks on k-anonymized microdata in a more rigorous way (than the previous work, including l-diversity), which can be used as a guideline and a predictor for efficient privacy disclosure control during k-anonymization process on a pre-published microdata set. Based on this theory, we uncover and define several main inference attacks classes in aspect of the various "knowledge" used by intruders. In experiments, we successfully implement the probabilistic inference risks evaluation as factors considered in an anonymization cost metric as a more effective anti-inference k-anonymity solution.