Reversible privacy preserving data mining: a combination of difference expansion and privacy preserving

Authors:
Tung-Shou Chen;Wei-Bin Lee;Jeanne Chen;Yuan-Hung Kao;Pei-Wen Hou
Affiliations:
Department of Computer Science and Information Engineering, National Taichung University of Science and Technology, Taichung City, Taiwan, R.O.C. 404;Department of Information Engineering and Computer Science, Feng Chia University, Taichung City, Taiwan, R.O.C. 407;Department of Computer Science and Information Engineering, National Taichung University of Science and Technology, Taichung City, Taiwan, R.O.C. 404;Department of Information Engineering and Computer Science, Feng Chia University, Taichung City, Taiwan, R.O.C. 407;Department of Computer Science and Information Engineering, National Taichung University of Science and Technology, Taichung City, Taiwan, R.O.C. 404
Venue:
The Journal of Supercomputing
Year:
2013

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Abstract

Privacy Preserving Data Mining (PPDM) can prevent private data from disclosure in data mining. However, the current PPDM methods damaged the values of original data where knowledge from the mined data cannot be verified from the original data. In this paper, we combine the concept and technique based on the reversible data hiding to propose the reversible privacy preserving data mining scheme in order to solve the irrecoverable problem of PPDM. In the proposed privacy difference expansion (PDE) method, the original data is perturbed and embedded with a fragile watermark to accomplish privacy preserving and data integrity of mined data and to also recover the original data. Experimental tests are performed on classification accuracy, probabilistic information loss, and privacy disclosure risk used to evaluate the efficiency of PDE for privacy preserving and knowledge verification.