Securely Solving the Distributed Graph Coloring Problem

TL;DR

This paper introduces privacy-preserving protocols for securely solving the distributed graph coloring problem, a fundamental NP-hard combinatorial optimization challenge, using efficient methods that maintain data privacy and are experimentally validated.

Contribution

The paper presents novel secure protocols specifically designed for the distributed graph coloring problem, addressing privacy concerns in a computationally efficient manner.

Findings

Protocols are secure against various attack models.

Experimental results show high accuracy and efficiency.

Approach effectively preserves privacy during computation.

Abstract

Combinatorial optimization is a fundamental problem found in many fields. In many real life situations, the constraints and the objective function forming the optimization problem are naturally distributed amongst different sites in some fashion. A typical approach for solving such problem is to collect all of this information together and centrally solve the problem. However, this requires all parties to completely share their information, which may lead to serious privacy issues. Thus, it is desirable to propose a privacy preserving technique that can securely solve specific combinatorial optimization problems. A further complicating factor is that combinatorial optimization problems are typically NP-hard, requiring approximation algorithms or heuristics to provide a practical solution. In this paper, we focus on a very well-known hard problem -- the distributed graph coloring problem, which has been utilized to model many real world problems in scheduling and resource allocation. We propose efficient protocols to securely solve such fundamental problem. We analyze the security of our approach and experimentally demonstrate the effectiveness of our approach.