Protecting Privacy Against Regression Attacks in Predictive Data Mining
Xiao Bai Li, Sumit Sarkar · Journal of the Association for Information Systems · 2011
Regression techniques can be used not only for legitimate data analysis, but also to infer private information about individuals. In this paper, we demonstrate that regression trees, a popular data-mining technique, can be used to effectively reveal individuals' sensitive data. This problem, which we call a "regression attack," has been overlooked in the literature. Existing privacy-preserving techniques are not appropriate in coping with this problem. We propose a new approach to counter regression attacks. To protect against privacy disclosure, our approach adopts a novel measure which considers the tradeoff between disclosure risk and data utility in a regression tree pruning process. We also propose a dynamic value-concatenation method, which overcomes the limitation of requiring a user-defined generalization hierarchy in traditional k-anonymity approaches. Our approach can be used for anonymizing both numeric and categorical data. An experimental study is conducted to demonstrate the effectiveness of the proposed approach.