Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
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
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Achieving anonymity via clustering
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Utility-based anonymization using local recoding
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Anatomy: simple and effective privacy preservation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
The new Casper: query processing for location services without compromising privacy
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Preventing Location-Based Identity Inference in Anonymous Spatial Queries
IEEE Transactions on Knowledge and Data Engineering
On static and dynamic methods for condensation-based privacy-preserving data mining
ACM Transactions on Database Systems (TODS)
ANGEL: Enhancing the Utility of Generalization for Privacy Preserving Publication
IEEE Transactions on Knowledge and Data Engineering
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Anatomy is a popular technique for privacy preserving in data publication. However, anatomy is fragile under background knowledge attack and can only be applied into limited applications. To overcome these drawbacks, we develop an improved version of anatomy: permutation anonymization, a new anonymization technique that is more effective than anatomy in privacy protection, and meanwhile is able to retain significantly more information in the microdata. We present the detail of the technique and build the underlying theory of the technique. Extensive experiments on real data are conducted, showing that our technique allows highly effective data analysis, while offering strong privacy guarantees.