Performance measurements for privacy preserving data mining

Authors:
Nan Zhang;Wei Zhao;Jianer Chen
Affiliations:
Department of Computer Science, Texas A&M University, College Station, TX;Department of Computer Science, Texas A&M University, College Station, TX;Department of Computer Science, Texas A&M University, College Station, TX
Venue:
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Year:
2005

Citing 7
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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.