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 the complexity of optimal K-anonymity
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
\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
Anatomy: simple and effective privacy preservation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
M-invariance: towards privacy preserving re-publication of dynamic datasets
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Privacy skyline: privacy with multidimensional adversarial knowledge
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
The cost of privacy: destruction of data-mining utility in anonymized data publishing
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Composition attacks and auxiliary information in data privacy
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Distribution-preserving statistical disclosure limitation
Computational Statistics & Data Analysis
Distribution based microdata anonymization
Proceedings of the VLDB Endowment
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part II
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Although there are a number of anonymization techniques in the microdata publication, two problems remain: (1) the privacy breaches with auxiliary knowledge; (2) the large information losses during the anonymization. We establish the requirement of presence anonymity and propose the two-step process of synthesizing, consisting of learning a model from the original data, and then sampling a published version with it, which has the similar statistical characteristics and includes fake records. The advantage is that it prevents the auxiliary knowledge attacks as well as enables researchers get correct or approximately correct conclusions. Furthermore, its effectiveness is proved through extensive experiments.