Personalized extended (˛, k)-anonymity model forrnprivacy-preserving data publishing

摘要

General (α, k)-anonymity model is a widely used method in privacy-preserving data publishing, but it cannotrnprovide personalized anonymity. At present, two main schemes for personalized anonymity are thernindividual-oriented anonymity and the sensitive value-oriented anonymity. Unfortunately, the existing personalizedrnanonymity models, designed for any of the aforementioned schemes for privacy-preserving datarnpublishing, are not effective enough to meet the personalized privacy preservation requirement. In thisrnpaper, we propose a novel personalized extended scheme to provide the personalized services in generalrn(α, k)-anonymity model. The sensitive value-oriented anonymity is combined with the individual-orientedrnanonymity in the new personalized extended (α, k)-anonymity model by the following two steps: (1) Thernsensitive attribute values are divided into several groups according to their sensitivities, and each group isrnassigned with its own frequency constraint threshold. (2) A guarding node is set for each individual to replacernhis/her sensitive value if necessary.We implement the personalized extended (α, k)-anonymity model with arnclustering algorithm. The performance evaluation finally shows that our model can provide stronger privacyrnpreservation efficiently as well as achieving the personalized service. Copyright © 2016 JohnWiley & Sons,rnLtd.