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Data privacy through optimal k anonymization

Data privacy through optimal k anonymization

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Data privacy through optimal k anonymization

Bayardo R and Agrawal R. Data privacy through optimal k-anonymization , In Proceedings of the 21st IEEE International Conference on Data Engineering. 2005: 217--228.

the greater the implied privacy since no individual can be identified with probability exceeding 1/k through Our algorithm finds optimal k -anonymization on practical real-life data sets.

Agrawal. l Conf. on Data Engineering (ICDE 2005) , Tokyo, Japan, April 2005. [ PDF

Data Privacy through Optimal k-Anonymization, Roberto J. Bayardo, Rakesh Agrawal: Bootstrapping Semantic Annotations for Content-Rich HTML Documents, Saikat Mukherjee, I.V.

Fung, Ke Wang (Simon Fraser University), Philip S. Yu (IBM Watson Research Center) Data Privacy through Optimal k-Anonymization: Roberto J. Bayardo, Rakesh Agrawal (IBM Almaden Research Center)

Data Privacy through Optimal k-Anonymization. ICDE 2005 : 217-228: 31: EE: Roberto J. Bayardo Jr., Sebastian Thomschke : Exploiting the web for point-in-time file sharing. WWW (Special interest tracks and

Data Privacy through Optimal k-Anonymization. 217-228. Electronic Edition (link) BibTeX. P2P and Data Dissemination. Cristiana Amza , Alan L. Cox , Willy Zwaenepoel

J., and Agrawal, R. (2005), ?Data Privacy Through Optimal K-Anonymization,? IEEE 2005 . International Conference on Data Engineering . Bethlehem, J.

auditing, database queries, etc.) z Privacy through distributed data mining Bayes-Optimal Privacy? Drawbacks. z Insufficient Relative condensation loss L(S) through k-anonymization

Data Privacy through Optimal k-Anonymization (Abstract) Roberto J. Bayardo, IBM Almaden Research Center Rakesh Agrawal, IBM Almaden Research Center pp. 217-228

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