A close look at privacy preserving data mining methods

Xiaodan Wu, Yunfeng Wang, Chao Hsien Chu, Fengli Liu, Ping Chen, Dianmin Yue

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Recent advances in information, communications, data mining, and security technologies have gave rise to a new era of research, known as privacy preserving data mining (PPDM). Several data mining algorithms, incorporating privacy preserving mechanisms, have been developed that allow one to extract relevant knowledge from large amount of data, while hide sensitive data or information from disclosure or inference. PPDM is a new attempt; thus, several research questions have often being asked. For instance: (1) how to measure the performance of these algorithms? (2) how effective of these algorithms in terms of privacy preserving? (3) will they impact the accuracy of data mining results? And (4) which one can better protect sensitive information? To help answer these questions, we conduct an extensive review on literature. We present a classification scheme, adopted from early studies, to guide the review process. Finally, we share directions for future research.

Original languageEnglish (US)
Title of host publicationPACIS 2006 - 10th Pacific Asia Conference on Information Systems: ICT and Innovation Economy
Pages167-173
Number of pages7
StatePublished - 2006
Event10th Pacific Asia Conference on Information Systems: ICT and Innovation Economy, PACIS 2006 - Kuala Lumpur, Malaysia
Duration: Jul 6 2006Jul 9 2006

Other

Other10th Pacific Asia Conference on Information Systems: ICT and Innovation Economy, PACIS 2006
CountryMalaysia
CityKuala Lumpur
Period7/6/067/9/06

All Science Journal Classification (ASJC) codes

  • Information Systems

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    Wu, X., Wang, Y., Chu, C. H., Liu, F., Chen, P., & Yue, D. (2006). A close look at privacy preserving data mining methods. In PACIS 2006 - 10th Pacific Asia Conference on Information Systems: ICT and Innovation Economy (pp. 167-173)