Privacy-Preserving Sharing of Horizontally-Distributed Private Data for Constructing Accurate Classifiers

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 privacypreserving 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 origina...

• Type: Conference
• Year: 2007
• Where: KDD
• Authors: Vincent Yan Fu Tan, See-Kiong Ng
• Keywords: Data Mining, Data Sanitization Algorithm, KDD 2007, Necessary Data Integrity, Original Private Data