Abstract #301553

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JSM 2003 Abstract #301553
Activity Number: 156
Type: Invited
Date/Time: Monday, August 4, 2003 : 2:00 PM to 3:50 PM
Sponsor: Business & Economics Statistics Section
Abstract - #301553
Title: Privacy Sensitive Data Mining
Author(s): Rakesh Agrawal*+
Companies: Almaden Research Center
Address: 650 Harry Rd., San Jose, CA, 95120-6099,
Keywords: data mining ; privacy ; decision trees ; classification
Abstract:

The explosive progress in networking, storage, and processor technologies is resulting in an unprecedented amount of digitization of information. In concert with this dramatic increase in digital data, concerns about the privacy of personal information have emerged globally. Data mining, with its promise to efficiently discover valuable, nonobvious information from large databases, is particularly vulnerable to misuse. One way of preserving privacy of individual data records would be to perturb them. Since the primary task in data mining is the development of models about aggregated data, we explore if we can develop accurate models without access to precise information in individual data records. We consider the concrete case of building a decision-tree classifier from perturbed data. While it is not possible to accurately estimate original values in individual data records, we describe a reconstruction procedure to accurately estimate the distribution of original data values. By using these reconstructed distributions, we are able to build classifiers whose accuracy is comparable to the accuracy of classifiers built with the original data.

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