Optimal Accuracy-Privacy Trade-Off for Secure Multi-Party Computations

TL;DR

This paper introduces a new measure for quantifying information leakage in secure multi-party computations, proposes a function substitution method to balance privacy and accuracy, and demonstrates how to achieve optimal trade-offs.

Contribution

It presents a generalized leakage measure, a function substitution approach for privacy control, and theoretical bounds for privacy gains in secure computations.

Findings

The new leakage measure effectively quantifies privacy loss.

Function substitution improves confidentiality while maintaining output accuracy.

Optimal privacy-accuracy trade-offs can be achieved through the proposed method.

Abstract

The purpose of Secure Multi-Party Computation is to enable protocol participants to compute a public function of their private inputs while keeping their inputs secret, without resorting to any trusted third party. However, opening the public output of such computations inevitably reveals some information about the private inputs. We propose a measure generalising both Renyi entropy and g-entropy so as to quantify this information leakage. In order to control and restrain such information flows, we introduce the notion of function substitution which replaces the computation of a function that reveals sensitive information with that of an approximate function. We exhibit theoretical bounds for the privacy gains that this approach provides and experimentally show that this enhances the confidentiality of the inputs while controlling the distortion of computed output values. Finally, we investigate the inherent compromise between accuracy of computation and privacy of inputs and we demonstrate how to realise such optimal trade-offs.