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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Agrawal, D., Aggarwal, C.C.: On the design and quantification of privacy preserving data mining algorithms. In: Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, pp. 247–255. ACM Press, New York (2001)
Agrawal, R., Srikant, R.: Privacy-preserving data mining. In: Proceedings of the 2000 ACM SIGMOD international conference on Management of data, pp. 439–450. ACM Press, New York (2000)
Du, W., Atallah, M.: Privacy-preserving cooperative statistical analysis. In: Proceedings of the 17th Annual Computer Security Applications Conference, Washington, DC, USA, p. 102. IEEE Computer Society, Los Alamitos (2001)
Du, W., Zhan, Z.: Using randomized response techniques for privacy-preserving data mining. In: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 505–510. ACM Press, New York (2003)
Evfimievski, A., Gehrke, J., Srikant, R.: Limiting privacy breaches in privacy preserving data mining. In: Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, pp. 211–222. ACM Press, New York (2003)
Evfimievski, A., Srikant, R., Agarwal, R., Gehrke, J.: Privacy preserving mining of association rules. Inf. Syst. 29(4), 343–364 (2004)
Massey, F.: The kolmogorov-smirnov test for goodness of fit. Journal of the American Statistical Association 46(253)
Rizvi, S., Haritsa, J.: Maintaining data privacy in association rule mining (2002)
Zhang, N., Zhao, W., Chen, J.: On the performance measurement for privacy preserving data mining. Technical report (2004)
Zhu, Y., Liu, L.: Optimal randomization for privacy preserving data mining. In: Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 761–766. ACM Press, New York (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, N., Zhao, W., Chen, J. (2005). Performance Measurements for Privacy Preserving Data Mining. In: Ho, T.B., Cheung, D., Liu, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2005. Lecture Notes in Computer Science(), vol 3518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11430919_7
Download citation
DOI: https://doi.org/10.1007/11430919_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26076-9
Online ISBN: 978-3-540-31935-1
eBook Packages: Computer ScienceComputer Science (R0)