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Author Xiaojun Ye ♦ Yawei Zhang ♦ Ming Liu
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2008
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Computer programming, programs & data
Subject Keyword Data privacy ♦ Privacy ♦ Microdata release ♦ Taxonomy ♦ Data models ♦ Distance measurement ♦ Cancer ♦ Diseases ♦ k-anonymity
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.
Description Author affiliation: Sch. of Software, Tsinghua Univ., Beijing (Xiaojun Ye; Yawei Zhang; Ming Liu)
ISBN 9780769531854
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2008-07-20
Publisher Place China
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 339.85 kB
Page Count 8
Starting Page 341
Ending Page 348


Source: IEEE Xplore Digital Library