Paper 2021/082

Grades of Trust in Multiparty Computation

Jaskaran V. Singh and Nicholas Hopper

Abstract

Secure Multiparty Computation involves a protocol between parties with an aim to produce a computed result just as a trusted party would produce if the parties provided their inputs to it. The trusted party in conventional computation is replaced with "un-trusted" parties in Secure Multiparty Computation. We first show that this existing binary definition of trust is inadequate. Real world is rife with disparities, that which produce a perceivable trust gradient between the participants. Conventional MPC models do not take this into account and rather provide security guarantees based on the thresholds of the number of corrupted parties. The thresholds are supposed to cover for some of the parties turning out to be corrupt. Often, with the knowledge of prior probability of a party being corrupt, we can do better if we allot weight to each party based on how trusted we perceive it to be. Our paper explores this idea and our contributions towards it are three folds. First, we introduce the Graded Trust model where each party essentially has a "trust grade" assigned to it in the protocol based on the prior of it being corrupt. Second, we present a method for protocol translation and execution by by simulating players. Lastly, we present a discussion on the philosophy behind graded trust, and the potential benefits for large scale public MPC systems.