A Private and Finite-Time Algorithm for Solving a Distributed System of Linear Equations

TL;DR

This paper introduces TITAN, a fast distributed algorithm that solves linear systems over networks while ensuring privacy against adversaries, with proven finite-time convergence and applicability to least squares solutions.

Contribution

The paper presents a novel privacy-preserving distributed linear system solver with finite-time convergence, leveraging TITAN for secure average consensus over directed graphs.

Findings

Converges to least squares solution in finite rounds.

Protects local data privacy against honest-but-curious adversaries.

Outperforms state-of-the-art methods in computation, communication, and memory costs.

Abstract

This paper studies a system of linear equations, denoted as $Ax = b$, which is horizontally partitioned (rows in $A$ and $b$) and stored over a network of $m$ devices connected in a fixed directed graph. We design a fast distributed algorithm for solving such a partitioned system of linear equations, that additionally, protects the privacy of local data against an honest-but-curious adversary that corrupts at most $\tau$ nodes in the network. First, we present TITAN, privaTe fInite Time Average coNsensus algorithm, for solving a general average consensus problem over directed graphs, while protecting statistical privacy of private local data against an honest-but-curious adversary. Second, we propose a distributed linear system solver that involves each agent/devices computing an update based on local private data, followed by private aggregation using TITAN. Finally, we show convergence of our solver to the least squares solution in finite rounds along with statistical privacy of local linear equations against an honest-but-curious adversary provided the graph has weak vertex-connectivity of at least $\tau+1$. We perform numerical experiments to validate our claims and compare our solution to the state-of-the-art methods by comparing computation, communication and memory costs.