Performance Measurements for Privacy Preserving Data Mining

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 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.

• Type: Conference
• Year: 2005
• Where: PAKDD
• Authors: Nan Zhang 0004, Wei Zhao, Jianer Chen
• Keywords: Data Mining, Effective Sample Size, PAKDD 2005, Privacy Preserving Data Mining, Privacy Protection