Performance measurements for privacy preserving data mining

Nan Zhang, Wei Zhao, Jianer Chen

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

7 Scopus citations

Abstract

This paper establishes the foundation for the performance measurements of privacy preserving data mining techniques. The performance is measured in terms of the accuracy of data mining results and the privacy protection of sensitive data. On the accuracy side, we address the problem of previous measures and propose a new measure, named "effective sample size", to solve this problem. We show that our new measure can be bounded without any knowledge of the data being mined, and discuss when the bound can be met. On the privacy protection side, we identify a tacit assumption made by previous measures and show that the assumption is unrealistic in many situations. To solve the problem, we introduce a game theoretic framework for the measurement of privacy.

Original languageEnglish (US)
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 9th Pacific-Asia Conference, PAKDD 2005, Proceedings
PublisherSpringer Verlag
Pages43-49
Number of pages7
ISBN (Print)3540260765, 9783540260769
DOIs
StatePublished - 2005
Event9th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2005 - Hanoi, Viet Nam
Duration: May 18 2005May 20 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3518 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2005
CountryViet Nam
CityHanoi
Period5/18/055/20/05

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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