Performance Measurements for Privacy Preserving Data Mining

• Published in: Advances in Knowledge Discovery and Data Mining pp. 43 - 49
• Main Authors: Zhang, Nan, Zhao, Wei, Chen, Jianer
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Data Mining Task; Data processing. List processing. Character string processing; Data Provider; Effective Sample Size; Exact sciences and technology; Learning and adaptive systems; Memory organisation. Data processing; Privacy Protection; Randomization Operator; Software; Private life; Data privacy; Data analysis; Sample size; Knowledge discovery; Information extraction; Data mining; Game theory
• ISBN: 9783540260769; 3540260765
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11430919_7

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.