Data mining tasks such as supervised classification can often benefit from a large training dataset. However, in many application domains, privacy concerns can hinder the construction of an accurate classifier by combining datasets from multiple sites. In this work, we propose a novel privacy-preserving distributed data sanitization algorithm that randomizes the private data at each site independently before the data is pooled to form a classifier at a centralized site. Distance-preserving perturbation approaches have been proposed by other researchers but we show that they can be susceptible to security risks. To enhance security, we require a unique non-distance-preserving approach. We use Kernel Density Estimation (KDE) Resampling, where samples are drawn independently from a distribution that is approximately equal to the original data's distribution. KDE Resampling provides consistent density estimates with randomized samples that are asymptotically independent of the original samples. This ensures high accuracy, especially when a large number of samples is available, with low privacy loss. We evaluated our approach on five standard datasets in a distributed setting using three different classifiers. The classification errors only deteriorated by 3% (in the worst case) when we used the randomized data instead of the original private data. With a large number of samples, KDE Resampling effectively preserves privacy (due to the asymptotic independence property) and also maintains the necessary data integrity for constructing accurate classifiers (due to consistency).
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