Privacy Preserving Data Mining Research: Current Status and Key Issues

Authors:
Xiaodan Wu;Chao-Hsien Chu;Yunfeng Wang;Fengli Liu;Dianmin Yue
Affiliations:
School of Management, Hebei University of Technology, Tianjin 300130, China;College of Information Sciences and Technology, The Pennsylvania State University, 301K IST Building, University Park, PA 16802, USA;School of Management, Hebei University of Technology, Tianjin 300130, China;School of Management, Hebei University of Technology, Tianjin 300130, China;School of Management, Hebei University of Technology, Tianjin 300130, China
Venue:
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
Year:
2007

Citing 24
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Abstract

Recent advances in the Internet, in data mining, and in security technologies have gave rise to a new stream of research, known as privacy preserving data mining (PPDM). PPDM technologies allow us to extract relevant knowledge from a large amount of data, while hide sensitive data or information from disclosure. Several research questions have often being asked: (1) what kind of option available for privacy preserving? (2) Which method is more popular? (3) how to measure the performance of these algorithms? And (4) how effective of these algorithms in preserving privacy? To help answer these questions, we conduct an extensive review of 29 recent references from years 2000 to 2006 for analysis.