Differentially Private ADMM-Based Distributed Discrete Optimal Transport for Resource Allocation

TL;DR

This paper introduces a differentially private distributed algorithm for optimal transport in resource allocation, combining ADMM with output perturbation to protect sensitive information during network updates.

Contribution

It develops a novel privacy-preserving distributed ADMM algorithm for optimal transport, ensuring differential privacy in resource allocation tasks.

Findings

The proposed scheme is differentially private against inference attacks.

The algorithm effectively balances privacy and transport efficiency.

Case studies demonstrate practical applicability and robustness.

Abstract

Optimal transport (OT) is a framework that can guide the design of efficient resource allocation strategies in a network of multiple sources and targets. To ease the computational complexity of large-scale transport design, we first develop a distributed algorithm based on the alternating direction method of multipliers (ADMM). However, such a distributed algorithm is vulnerable to sensitive information leakage when an attacker intercepts the transport decisions communicated between nodes during the distributed ADMM updates. To this end, we propose a privacy-preserving distributed mechanism based on output variable perturbation by adding appropriate randomness to each node's decision before it is shared with other corresponding nodes at each update instance. We show that the developed scheme is differentially private, which prevents the adversary from inferring the node's confidential information even knowing the transport decisions. Finally, we corroborate the effectiveness of the devised algorithm through case studies.