Premium
Personalized extended ( α , k )‐anonymity model for privacy‐preserving data publishing
Author(s) -
Liu Xiangwen,
Xie Qingqing,
Wang Liangmin
Publication year - 2016
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.3886
Subject(s) - anonymity , computer science , data publishing , k anonymity , internet privacy , computer security , publishing , political science , law
Summary General ( α , k )‐anonymity model is a widely used method in privacy‐preserving data publishing, but it cannot provide personalized anonymity. At present, two main schemes for personalized anonymity are the individual‐oriented anonymity and the sensitive value‐oriented anonymity. Unfortunately, the existing personalized anonymity models, designed for any of the aforementioned schemes for privacy‐preserving data publishing, are not effective enough to meet the personalized privacy preservation requirement. In this paper, we propose a novel personalized extended scheme to provide the personalized services in general ( α , k )‐anonymity model. The sensitive value‐oriented anonymity is combined with the individual‐oriented anonymity in the new personalized extended ( α , k )‐anonymity model by the following two steps: (1) The sensitive attribute values are divided into several groups according to their sensitivities, and each group is assigned with its own frequency constraint threshold. (2) A guarding node is set for each individual to replace his/her sensitive value if necessary. We implement the personalized extended ( α , k )‐anonymity model with a clustering algorithm. The performance evaluation finally shows that our model can provide stronger privacy preservation efficiently as well as achieving the personalized service. Copyright © 2016 John Wiley & Sons, Ltd.