Abstract
Randomized Response techniques have been empirically investigated in privacy preserving association rule mining. In this paper, we investigate the accuracy (in terms of bias and variance of estimates) of both support and confidence estimates of association rules derived from the randomized data. We demonstrate that providing confidence on data mining results from randomized data is significant to data miners. We propose the novel idea of using interquantile range to bound those estimates derived from the randomized market basket data. The performance is evaluated using both representative real and synthetic data sets.
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.
Similar content being viewed by others
References
Agrawal, D., Agrawal, C.: On the design and quantification of privacy preserving data mining algorithms. In: Proceedings of the 20th Symposium on Principles of Database Systems (2001)
Agrawal, R., Srikant, R.: Privacy-preserving data mining. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, Dallas, Texas, May 2000, pp. 439–450 (2000)
Agrawal, S., Haritsa, J.: A framework for high-accuracy privacy-preserving mining. In: Proceedings of the 21st IEEE International Conference on Data Engineering, pp. 193–204 (2005)
Agrawal, S., Krishnan, V., Haritsa, J.: On addressing efficiency concerns in privacy-preserving mining. In: Lee, Y., Li, J., Whang, K.-Y., Lee, D. (eds.) DASFAA 2004. LNCS, vol. 2973, pp. 113–124. Springer, Heidelberg (2004)
Chaudhuri, A., Mukerjee, R.: Randomized Response Theory and Techniques. Marcel Dekker, New York (1988)
den Hout, A.V., der Heijden, P.G.M.V.: Randomized response, statistical disclosure control and misclassification: A review. International Statistical Review 70(2), 269–288 (2002)
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 (2003)
Evfimievski, A., Srikant, R., Agrawal, R., Gehrke, J.: Privacy preserving mining of association rules. In: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 217–228 (2002)
Guo, L., Guo, S., Wu, X.: On addressing accuracy concerns in privacy preserving association rule mining. Technical Report, CS Dept., UNC Charlotte (March 2007)
Huang, Z., Du, W., Chen, B.: Deriving private information from randomized data. In: Proceedings of the ACM SIGMOD Conference on Management of Data, Baltimore, MA (2005)
Kargupta, H., Datta, S., Wang, Q., Sivakumar, K.: On the privacy preserving properties of random data perturbation techniques. In: Proceedings of the 3rd International Conference on Data Mining, pp. 99–106 (2003)
Kendall, M.G., Stuart, A.: The advanced theory of statistics, vol. 1. Hafner Pub. Co., New York (1969)
Rizvi, S., Haritsa, J.: Maintaining data privacy in association rule mining. In: Proceedings of the 28th International Conference on Very Large Data Bases (2002)
Springer, M.D.: The Algebra of Random Variables. John Wiley and Sons, New York (1979)
Warner, S.: Randomized response: a survey technique for eliminating evasive answer bias. J. Amer. Statist. Assoc. 60, 63–69 (1965)
Wu, X., Barbará, D., Ye, Y.: Screening and interpreting multi-item associations based on log-linear modeling. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washinton, August 2003, pp. 276–285 (2003)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Guo, L., Guo, S., Wu, X. (2008). On Addressing Accuracy Concerns in Privacy Preserving Association Rule Mining. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2008. Lecture Notes in Computer Science(), vol 5012. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68125-0_13
Download citation
DOI: https://doi.org/10.1007/978-3-540-68125-0_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68124-3
Online ISBN: 978-3-540-68125-0
eBook Packages: Computer ScienceComputer Science (R0)