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http://dx.doi.org/10.13089/JKIISC.2007.17.3.69

An Effective Anonymization Management under Delete Operation of Secure Database  

Byun, Chang-Woo (Department of Computer Science, Sogang University)
Kim, Jae-Whan (Department of Computer Science, Sogang University)
Lee, Hyang-Jin (Cryptography & Standardization Team, Korea Information Security)
Kang, Yeon-Jung (Cryptography & Standardization Team, Korea Information Security)
Park, Seog (Department of Computer Science, Sogang University)
Publication Information
Journal of the Korea Institute of Information Security & Cryptology / v.17, no.3, 2007 , pp. 69-80 More about this Journal
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.
Keywords
privacy; publishing data; inference attack; anonymity; diversity; data quality;
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