Privacy preserving data mining research: Current status and key issues

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

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

18 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationComputational Science - ICCS 2007 - 7th International Conference, Proceedings
PublisherSpringer Verlag
Number of pages11
EditionPART 3
ISBN (Print)9783540725879
StatePublished - 2007
Event7th International Conference on Computational Science, ICCS 2007 - Beijing, China
Duration: May 27 2007May 30 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume4489 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other7th International Conference on Computational Science, ICCS 2007

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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