Privacy-preserving data mining

TLDR

Future data mining research should develop techniques that address privacy concerns. The study investigates whether accurate models can be built without precise individual data and proposes a reconstruction method to estimate original data distributions. They construct decision‑tree classifiers from perturbed data and employ a reconstruction procedure to recover the distribution of original values. Despite the perturbed records differing markedly from the originals, the reconstructed distributions enable classifiers to achieve accuracy comparable to those trained on unperturbed data.

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 training 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.