Chang-Woo Byun, Jae-Whan Kim, Hyang-Jin Lee, Yeon-Jung Kang, Seog Park,

Vol. 17, No. 3, pp. 69-80, Jun. 2007
10.13089/JKIISC.2007.17.3.69, Full Text:
Keywords: Privacy, publishing data, inference attack, Anonymity, diversity, data quality
Abstract

To protect personal information when releasing data, a general privacy-protecting technique is the removal of all the explicit identifiers, such as names and social security numbers. De-identifying data, however, provides no guarantee of anonymity because released information can be linked to publicly available information to identify them and to infer information that was not intended for release. In recent years, two emerging concepts in personal information protection are k-anonymity and $\ell$-diversity, which guarantees privacy against homogeneity and background knowledge attacks. While these solutions are signigicant in static data environment, they are insufficient in dynamic environments because of vulnerability to inference. Specially, the problem appeared in record deletion is to deconstruct the k-anonymity and $\ell$-diversity. In this paper, we present an approach to securely anonymizing a continuously changeable dataset in an efficient manner while assuring high data quality.

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