Analyzing Different Multiparty Computation Techniques

Abstract

Strong encryption provides support to data privacy. Although encryption can make the data secure in data transfer and at rest, at some point this encrypted data definitely needs to be decrypted. Now at this very point, it so happens that the data becomes susceptible to attacks ultimately resulting in compromised data privacy. This is where secure multiparty computation (MPC) comes into picture; thereby, it provides ability to calculate required values from numerous encrypted data sources without any party compromising on their secret data. At ground level, MPC is a very general concept that can be realized using different protocols, such as secret sharing, in which secret data from each party is divided and then distributed randomly, encrypted “shares” among the parties. This distributed data when eventually aggregated would provide the final desired result. If anyone happens to intersect the data at hand of any of the parties, it would prove futile. With this paper, we focus on different algorithms or techniques that work behind the scenes in implementing MPC. These techniques include homomorphic encryption, followed by RSA combined with Paillier’s algorithm and lastly the concept of garbled circuits.