Fusion: Privacy-Preserving Distributed Protocol for High-Dimensional Data Mashup

Abstract

In the last decade, several approaches concerning private data release for data mining have been proposed. Data mashup, on the other hand, has recently emerged as a mechanism for integrating data from several data providers. Fusing both techniques to generate mashup data in a distributed environment while providing privacy and utility guarantees on the output involves several challenges. That is, how to ensure that no unnecessary information is leaked to the other parties during the mashup process, how to ensure the mashup data is protected against certain privacy threats, and how to handle the high-dimensional nature of the mashup data while guaranteeing high data utility. In this paper, we present Fusion, a privacy-preserving multi-party protocol for data mashup with guaranteed LKC-privacy for the purpose of data mining. Experiments on real-life data demonstrate that the anonymous mashup data provide better data utility, the approach can handle high dimensional data, and it is scalable with respect to the data size.