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Evolutionary algorithms (EAs), such as the genetic algorithm (GA), offer anelegant way to handle combinatorial optimization problems (COPs). However,limited by expertise and resources, most users do not have enough capability toimplement EAs to solve COPs. An intuitive and promising solution is tooutsource evolutionary operations to a cloud server, whilst it suffers fromprivacy concerns. To this end, this paper proposes a novel computing paradigm,evolution as a service (EaaS), where a cloud server renders evolutionarycomputation services for users without sacrificing users' privacy. Inspired bythe idea of EaaS, this paper designs PEGA, a novel privacy-preserving GA forCOPs. Specifically, PEGA enables users outsourcing COPs to the cloud serverholding a competitive GA and approximating the optimal solution in aprivacy-preserving manner. PEGA features the following characteristics. First,any user without expertise and enough resources can solve her COPs. Second,PEGA does not leak contents of optimization problems, i.e., users' privacy.Third, PEGA has the same capability as the conventional GA to approximate theoptimal solution. We implements PEGA falling in a twin-server architecture andevaluates it in the traveling salesman problem (TSP, a widely known COP).Particularly, we utilize encryption cryptography to protect users' privacy andcarefully design a suit of secure computing protocols to support evolutionaryoperators of GA on encrypted data. Privacy analysis demonstrates that PEGA doesnot disclose the contents of the COP to the cloud server. Experimentalevaluation results on four TSP datasets show that PEGA is as effective as theconventional GA in approximating the optimal solution.

Evolution as a Service: A Privacy-Preserving Genetic Algorithm for Combinatorial Optimization