On the Design and Quantification of Privacy Preserving Data Mining Algorithms

The increasing ability to track and collect large amounts of data with the use of current hardware technology has lead to an interest in the development of data mining algorithms which preserve user privacy. A recently proposed technique addresses the issue of privacy preservation by perturbing the data and reconstructing distributions at an aggregate level in order to perform the mining. This method is able to retain privacy while accessing the information implicit in the original attributes. The distribution reconstruction process naturally leads to some loss of information which is acceptable in many practical situations. This paper discusses an Expectation Maximization (EM) algorithm for distribution reconstruction which is more effective than the currently available method in terms of the level of information loss. Specifically, we prove that the EM algorithm converges to the maximum likelihood estimate of the original distribution based on the perturbed data. We show that when a...

• Type: Conference
• Year: 2001
• Where: PODS
• Authors: Dakshi Agrawal, Charu C. Aggarwal
• Keywords: Data Mining Algorithms, Database, PODS 2001, Privacy Metrics, Privacy Preserving Data