Differentially Private Secure Multiplication: Beyond Two Multiplicands

TL;DR

This paper extends the study of differentially private secure multiplication to multiple multiplicands, proposing a new framework that improves accuracy while maintaining privacy in distributed systems with colluding nodes.

Contribution

It introduces a generalized secure multiplication scheme for multiple inputs, with optimized privacy-accuracy trade-offs and layered noise injection techniques.

Findings

Characterized optimal privacy-accuracy trade-off for N in [(M-1)T+1, MT]

Derived tight bounds for N = T+1 in high-privacy regime

Proposed a generalized encoding framework for multiple multiplicands

Abstract

We study the problem of differentially private (DP) secure multiplication in distributed computing systems, focusing on regimes where perfect privacy and perfect accuracy cannot be simultaneously achieved. Specifically, N nodes collaboratively compute the product of M private inputs while guaranteeing epsilon-DP against any collusion of up to T nodes. Prior work has characterized the fundamental privacy-accuracy trade-off for the multiplication of two multiplicands. In this paper, we extend these results to the more general setting of computing the product of an arbitrary number M of multiplicands. We propose a secure multiplication framework based on carefully designed encoding polynomials combined with layered noise injection. The proposed construction generalizes existing schemes and enables the systematic cancellation of lower-order noise terms, leading to improved estimation accuracy. We explore two regimes: (M-1)T+1 <= N <= MT and N = T+1. For (M-1)T+1 <= N <= MT, we characterize the optimal privacy--accuracy trade-off. When N = T+1, we derive nontrivial achievability and converse bounds that are asymptotically tight in the high-privacy regime.