Privacy preserving data mining algorithms by data distortion

Xiao Dan Wu, Dian Min Yue, Feng Li Liu, Yun Feng Wang, Chao Hsien Chu

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

8 Scopus citations

Abstract

Recently, a new class of data mining methods, known as privacy preserving data mining (PPDM) algorithms, has been developed by the research community working on security and knowledge discovery. The aim of these algorithms is the extraction of relevant knowledge from large amount of data, while protecting sensitive information simultaneously. In this paper, we present a generic PPDM framework and a classification scheme for centralized database, adopted from early studies, to guide the review process. Frequencies of different techniques/algorithms used are tableau and analyzed. A set of metrics and a theoretical framework are also proposed for assessing the relative performance of selected PPDM algorithms. Finally, we share directions for future research.

Original languageEnglish (US)
Title of host publicationProceedings of 2006 International Conference on Management Science and Engineering, ICMSE'06 (13th)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages223-228
Number of pages6
ISBN (Print)7560323553, 9787560323558
DOIs
StatePublished - Jan 1 2006
Event2006 International Conference on Management Science and Engineering, ICMSE'06 - Lille, France
Duration: Oct 5 2006Oct 7 2006

Publication series

NameProceedings of 2006 International Conference on Management Science and Engineering, ICMSE'06 (13th)

Other

Other2006 International Conference on Management Science and Engineering, ICMSE'06
CountryFrance
CityLille
Period10/5/0610/7/06

All Science Journal Classification (ASJC) codes

  • Management of Technology and Innovation
  • Decision Sciences(all)
  • Engineering (miscellaneous)

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