A New Efficient Privacy-Preserving Scalar Product Protocol

Abstract

Recently, privacy issues have become important in data analysis, especially when data is horizontally partitioned over several parties. In data mining, the data is typically represented as attribute-vectors and, for many applications, the scalar (dot) product is one of the fundamental operations that is repeatedly used. In privacy-preserving data mining, data is distributed across several parties. The efficiency of secure scalar products is important, not only because they can cause overhead in communication cost, but dot product operations also serve as one of the basic building blocks for many other secure protocols. Although several solutions exist in the relevant literature for this problem, the need for more efficient and more practical solutions still remains. In this paper, we present a very efficient and very practical secure scalar product protocol. We compare it to the most common scalar product protocols. We not only show that our protocol is much more efficient than the existing ones, we also provide experimental results by using a real life dataset.