Privacy Preserving Computation of Scalar Product and Sign of Scalar Product

Abstract

Scalar product is repeatedly used in a large family of applications. It is a useful building block of many scientific and business applications. Secure scalar product computation is widely used in many privacy-preserving computation domains. In this work, we have proposed privacy preserving protocols for computing scalar product or only its sign. These protocols work on two basic principles - random data perturbation and algebraic methods, and thus avoid standard encryption/decryption overheads. We have designed the protocols to get sign of the scalar product, which can act as a basic building block in privacy preserving database queries over distributed databases and many other applications. We have developed two sets of protocols for the two-party computation with and without using an untrusted third-party. Experimental results on generated random datasets show that the proposed protocols indeed involve low communication overhead.