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Privacy-Preserving Data Mining (2000)
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BibTeX
@MISC{Agrawal00privacy-preservingdata,
author = {Rakesh Agrawal and Ramakrishnan Srikant},
title = { Privacy-Preserving Data Mining },
year = {2000}
}
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Abstract
A fruitful direction for future data mining research will be the development of techniques that incorporate privacy concerns. Specifically, we address the following question. Since the primary task in data mining is the development of models about aggregated data, can we develop accurate models without access to precise information in individual data records? We consider the concrete case of building a decision-tree classifier from tredning data in which the values of individual records have been perturbed. The resulting data records look very different from the original records and the distribution of data values is also very different from the original distribution. While it is not possible to accurately estimate original values in individual data records, we propose a-novel 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.
Keyphrases
privacy-preserving data mining individual data record a-novel reconstruction procedure data record following question future data mining research decision-tree classifier data mining original data value original data original distribution primary task individual record original record privacy concern original value data value concrete case accurate model fruitful direction
