Privacy-Preserving Outlier Detection Through Random Nonlinear Data Distortion

— Consider a scenario in which the data owner has some private/sensitive data and wants a data miner to access it for studying important patterns without revealing the sensitive information. Privacy preserving data mining aims to solve this problem by randomly transforming the data prior to its release to data miners. Previous work only considered the case of linear data perturbations — additive, multiplicative or a combination of both for studying the usefulness of the perturbed output. In this paper, we discuss nonlinear data distortion using potentially nonlinear random data transformation and show how it can be useful for privacy preserving anomaly detection from sensitive datasets. We develop bounds on the expected accuracy of the nonlinear distortion and also quantify privacy by using standard definitions. The highlight of this approach is to allow a user to control the amount of privacy by varying the degree of nonlinearity. We show how our general transformation can be use...

• Type: Journal
• Year: 2011
• Where: TSMC
• Authors: Kanishka Bhaduri, Mark D. Stefanski, Ashok N. Srivastava
• Keywords: Data Miners, Nonlinear, Privacy, TSMC 2011