Full Citation

Title: A new anonymity model for privacy-preserving data publishing

Citation Type: Journal Article

Publication Year: 2014

ISBN:

ISSN:

DOI: 10.1109/CC.2014.6969710

NSFID:

PMCID:

PMID:

Abstract: 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.

Url: http://ieeexplore.ieee.org/document/6969710/

User Submitted?: No

Authors: Huang, Xuezhen; Liu, Jiqiang; Han, Zhen; Yang, Jun

Periodical (Full): China Communications

Issue: 9

Volume: 11

Pages: 47-59

Data Collections: IPUMS USA

Topics: Methodology and Data Collection

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