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 anovel
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.