Privacy-preserving data publishing （PPDP） is one of the hot issues in the field of the network security. The existing PPDP technique cannot deal with generality attacks, which explicitly contain the sensitivity atta...Privacy-preserving data publishing （PPDP） is one of the hot issues in the field of the network security. The existing PPDP technique cannot deal with generality attacks, which explicitly contain the sensitivity attack and the similarity attack. This paper proposes a novel model, （w,γ, k）-anonymity, to avoid generality attacks on both cases of numeric and categorical attributes. We show that the optimal （w, γ, k）-anonymity problem is NP-hard and conduct the Top-down Local recoding （TDL） algorithm to implement the model. Our experiments validate the improvement of our model with real data.展开更多
基金supported in part by Research Fund for the Doctoral Program of Higher Education of China(No.20120009110007)Program for Innovative Research Team in University of Ministry of Education of China (No.IRT201206)+3 种基金Program for New Century Excellent Talents in University(NCET-110565)the Fundamental Research Funds for the Central Universities(No.2012JBZ010)the Open Project Program of Beijing Key Laboratory of Trusted Computing at Beijing University of TechnologyBeijing Higher Education Young Elite Teacher Project(No. YETP0542)
文摘Privacy-preserving data publishing （PPDP） is one of the hot issues in the field of the network security. The existing PPDP technique cannot deal with generality attacks, which explicitly contain the sensitivity attack and the similarity attack. This paper proposes a novel model, （w,γ, k）-anonymity, to avoid generality attacks on both cases of numeric and categorical attributes. We show that the optimal （w, γ, k）-anonymity problem is NP-hard and conduct the Top-down Local recoding （TDL） algorithm to implement the model. Our experiments validate the improvement of our model with real data.
