Cryptographic techniques for privacy-preserving data mining

This work describes the results of secure distributed computation using generic constructions that can be applied to any function that has an efficient representation as a circuit, and discusses their efficiency and relevance to privacy preserving computation of data mining algorithms.

Abstract

Research in secure distributed computation, which was done as part of a larger body of research in the theory of cryptography, has achieved remarkable results. It was shown that non-trusting parties can jointly compute functions of their different inputs while ensuring that no party learns anything but the defined output of the function. These results were shown using generic constructions that can be applied to any function that has an efficient representation as a circuit. We describe these results, discuss their efficiency, and demonstrate their relevance to privacy preserving computation of data mining algorithms. We also show examples of secure computation of data mining algorithms that use these generic constructions.