Evolution as a Service: A Privacy-Preserving Genetic Algorithm for Combinatorial Optimization
Bowen Zhao, Wei-Neng Chen, Feng-Feng Wei, Ximeng Liu, Qingqi Pei, Jun, Zhang

TL;DR
This paper introduces PEGA, a privacy-preserving genetic algorithm service that allows users to outsource combinatorial optimization problems to the cloud without revealing sensitive data, maintaining solution quality.
Contribution
The paper presents PEGA, a novel privacy-preserving GA framework enabling secure outsourcing of COPs to the cloud with comparable effectiveness to traditional GAs.
Findings
PEGA effectively preserves user privacy during optimization.
PEGA achieves similar solution quality as conventional GA.
Experimental results on TSP datasets validate PEGA's efficiency.
Abstract
Evolutionary algorithms (EAs), such as the genetic algorithm (GA), offer an elegant way to handle combinatorial optimization problems (COPs). However, limited by expertise and resources, most users do not have enough capability to implement EAs to solve COPs. An intuitive and promising solution is to outsource evolutionary operations to a cloud server, whilst it suffers from privacy concerns. To this end, this paper proposes a novel computing paradigm, evolution as a service (EaaS), where a cloud server renders evolutionary computation services for users without sacrificing users' privacy. Inspired by the idea of EaaS, this paper designs PEGA, a novel privacy-preserving GA for COPs. Specifically, PEGA enables users outsourcing COPs to the cloud server holding a competitive GA and approximating the optimal solution in a privacy-preserving manner. PEGA features the following…
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Taxonomy
TopicsMetaheuristic Optimization Algorithms Research · Privacy-Preserving Technologies in Data · Error Correcting Code Techniques
Methodstravel james · Genetic Algorithms
