Privacy preserving distributed knowledge discovery: survey and future directions

Abstract

The aim of privacy preserving data mining algorithms is to extract relevant knowledge from large amounts of data while protecting at the same time sensitive information. In the first part of this paper, we discuss privacy preserving distributed data mining techniques and provide a survey on the state–of–the–art methods in this field. We have considered randomisation, k–anonymisation, and cryptographic techniques. In the second part of the paper, we have described our proposed system for privacy preserving knowledge discovery over distributed databases which is still under development phase. The system is designed to perform local operations (local mining) in each site. This produces intermediate data that can be used to obtain the final result without revealing the private information of any site. Our proposal is mainly based on association rule mining and cryptographic techniques.