A Condensation Approach to Privacy Preserving Data Mining
In recent years, privacy preserving data mining has become an important problem because of the large amount of personal data which is tracked by many business applications. In many cases, users are unwilling to provide personal information unless the privacy of sensitive information is guaranteed. In this paper, we propose a new framework for privacy preserving data mining of multi-dimensional data. Previous work for privacy preserving data mining uses a perturbation approach which reconstructs data distributions in order to perform the mining. Such an approach treats each dimension independently and therefore ignores the correlations between the different dimensions. In addition, it requires the development of a new distribution based algorithm for each data mining problem, since it does not use the multi-dimensional records, but uses aggregate distributions of the data as input. This leads to a fundamental re-design of data mining algorithms. In this paper, we will develop a new and flexible approach for privacy preserving data mining which does not require new problem-specific algorithms, since it maps the original data set into a new anonymized data set. This anonymized data closely matches the characteristics of the original data including the correlations among the different dimensions. We present empirical results illustrating the effectiveness of the method.
KeywordsClassification Accuracy Privacy Preserve Data Mining Algorithm Average Group Size Data Mining Problem
Unable to display preview. Download preview PDF.
- 1.Agrawal, R., Srikant, R.: Privacy Preserving Data Mining. In: Proceedings of the ACM SIGMOD Conference (2000)Google Scholar
- 2.Agrawal, D., Aggarwal, C.C.: On the Design and Quantification of Privacy Preserving Data Mining Algorithms. In: ACM PODS Conference (2002)Google Scholar
- 4.Clifton, C., Marks, D.: Security and Privacy Implications of Data Mining. In: ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pp. 15–19 (1996)Google Scholar
- 5.Clifton, C., Kantarcioglu, M., Vaidya, J.: Defining Privacy for Data Mining. In: National Science Foundation Workshop on Next Generation Data Mining, pp. 126–133 (2002)Google Scholar
- 6.Vaidya, J., Clifton, C.: Privacy Preserving Association Rule Mining in Vertically Partitioned Data. In: ACM KDD Conference (2002)Google Scholar
- 8.Estivill-Castro, V., Brankovic, L.: Data Swapping: Balancing privacy against precision in mining for logic rules. In: Mohania, M., Tjoa, A.M. (eds.) DaWaK 1999. LNCS, vol. 1676, pp. 389–398. Springer, Heidelberg (1999)Google Scholar
- 9.Evfimievski, A., Srikant, R., Agrawal, R., Gehrke, J.: Privacy Preserving Mining Of Association Rules. In: ACM KDD Conference (2002)Google Scholar
- 10.Hinneburg, D.A., Keim, D.A.: An Efficient Approach to Clustering in Large Multimedia Databases with Noise. In: ACM KDD Conference (1998)Google Scholar
- 11.Iyengar, V.S.: Transforming Data To Satisfy Privacy Constraints. In: ACM KDD Conference (2002)Google Scholar
- 15.Moore Jr., R.A.: Controlled Data-Swapping Techniques for Masking Public Use Microdata Sets. Statistical Research Division Report Series, RR 96-04, US Bureau of the Census, Washington D.C. (1996)Google Scholar
- 16.Rizvi, S., Haritsa, J.: Maintaining Data Privacy in Association Rule Mining. In: VLDB Conference (2002)Google Scholar
- 18.Samarati, P., Sweeney, L.: Protecting Privacy when Disclosing Information: k- Anonymity and its Enforcement Through Generalization and Suppression. In: Proceedings of the IEEE Symposium on Research in Security and Privacy (1998)Google Scholar