NDLI: A Personalized (a,k)-Anonymity Model

Content Provider	IEEE Xplore Digital Library
Author	Xiaojun Ye Yawei Zhang Ming Liu
Copyright Year	2008
Description	Author affiliation: Sch. of Software, Tsinghua Univ., Beijing (Xiaojun Ye; Yawei Zhang; Ming Liu)
Abstract	One important privacy principle is that an individual has the freedom to decide his/her own privacy preferences, which should be taken into account when data holders release their privacy preserving micro data. Nevertheless, current related k-anonymity model research focuses on protecting individual private information by using pre-defined constraint parameters specified by data holders. This paper introduces a personalized (alpha, k) model by introducing a vector for describing individual personalized privacy requirements corresponding to each value in the domain of sensitive attributes by data respondents, and propose an efficiency anonymization algorithm which combines the top down specialization for quasi-identifier anonymization and the local recoding technique for the sensitive attribute generalization based on its attribute taxonomy tree. Experimental results show that this approach can meet better personalized privacy requirements and keep the information loss low.
Starting Page	341
Ending Page	348
File Size	348005
Page Count	8
File Format	PDF
ISBN	9780769531854
DOI	10.1109/WAIM.2008.22
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data privacy Privacy Microdata release Taxonomy Data models Distance measurement Cancer Diseases k-anonymity
Content Type	Text
Resource Type	Article

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