A secure distributed framework for achieving k -anonymity

A secure distributed framework for achieving k -anonymity k -anonymity provides a measure of privacy protection by preventing re-identification of data to fewer than a group of k data items. While algorithms exist for producing k -anonymous data, the model has been that of a single source wanting to publish data. Due to privacy issues, it is common that data from different sites cannot be shared directly. Therefore, this paper presents a two-party framework along with an application that generates k -anonymous data from two vertically partitioned sources without disclosing data from one site to the other. The framework is privacy preserving in the sense that it satisfies the secure definition commonly defined in the literature of Secure Multiparty Computation. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

A secure distributed framework for achieving k -anonymity

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Publisher
Springer-Verlag
Copyright
Copyright © 2006 by Springer-Verlag
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-006-0008-z
Publisher site
See Article on Publisher Site

Abstract

k -anonymity provides a measure of privacy protection by preventing re-identification of data to fewer than a group of k data items. While algorithms exist for producing k -anonymous data, the model has been that of a single source wanting to publish data. Due to privacy issues, it is common that data from different sites cannot be shared directly. Therefore, this paper presents a two-party framework along with an application that generates k -anonymous data from two vertically partitioned sources without disclosing data from one site to the other. The framework is privacy preserving in the sense that it satisfies the secure definition commonly defined in the literature of Secure Multiparty Computation.

Journal

The VLDB JournalSpringer Journals

Published: Nov 1, 2006

References

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