Paper Info
Title
Personalized extended (α, k)-anonymity model for privacy-preserving data publishing.
Abstract
SummaryGeneral (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 u0026 Sons, Ltd.
Year
Venue
Field
2017
Concurrency and Computation: Practice and Experience
Data mining,Information retrieval,Computer science,k-anonymity,Data publishing,Anonymity,Cluster analysis,Distributed computing
DocType
Volume
Issue
Journal
29
6
Citations 
PageRank 
References 
2
0.37
18
Authors
3
Name
Order
Citations
PageRank
Xiangwen Liu152.14
Qing-Qing Xie271.50
Liangmin Wang324.42