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Appl. Sci. 2018, 8(5), 783; https://doi.org/10.3390/app8050783

A New Approach to Privacy-Preserving Multiple Independent Data Publishing

1
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
2
Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518055, China
3
Department of Computer Science, University of Sherbrooke, Sherbrooke, QC J1K2R1, Canada
*
Author to whom correspondence should be addressed.
Received: 17 April 2018 / Revised: 4 May 2018 / Accepted: 10 May 2018 / Published: 14 May 2018
(This article belongs to the Section Computer Science and Electrical Engineering)
View Full-Text   |   Download PDF [775 KB, uploaded 17 May 2018]   |  

Abstract

We study the problem of privacy preservation in multiple independent data publishing. An attack on personal privacy which uses independent datasets is called a composition attack. For example, a patient might have visited two hospitals for the same disease, and his information is independently anonymized and distributed by the two hospitals. Much of the published work makes use of techniques that reduce data utility as the price of preventing composition attacks on published datasets. In this paper, we propose an innovative approach to protecting published datasets from composition attack. Our cell generalization approach increases both protection of individual privacy from composition attack and data utility. Experimental results show that our approach can preserve more data utility than the existing methods. View Full-Text
Keywords: anonymization; composition attack; privacy preservation; data publishing anonymization; composition attack; privacy preservation; data publishing
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Hasan, A.S.M.T.; Jiang, Q.; Chen, H.; Wang, S. A New Approach to Privacy-Preserving Multiple Independent Data Publishing. Appl. Sci. 2018, 8, 783.

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