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PEGA: A Privacy-Preserving Genetic Algorithm for Combinatorial Optimization.

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    This study introduces privacy-preserving genetic algorithm (PEGA) for cloud-based evolutionary computation as a service (ECaaS). PEGA enables users to solve complex combinatorial optimization problems securely and effectively without revealing sensitive data.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Cryptography

    Background:

    • Combinatorial optimization problems (COPs) are challenging for users lacking expertise or resources.
    • Outsourcing these computations to the cloud raises significant privacy concerns.
    • Existing solutions often require specialized knowledge or compromise data confidentiality.

    Purpose of the Study:

    • To propose a novel computing paradigm, Evolutionary Computation as a Service (ECaaS).
    • To introduce a privacy-preserving genetic algorithm (PEGA) for COPs.
    • To enable secure and effective cloud-based optimization for users with limited resources or expertise.

    Main Methods:

    • Developed a twin-server architecture for PEGA.
    • Utilized encryption cryptography and secure computing protocols for GA operators on encrypted data.
    • Implemented and evaluated PEGA on the Traveling Salesman Problem (TSP) and 0/1 Knapsack Problem (KP).

    Main Results:

    • PEGA effectively empowers users to solve COPs without domain expertise or sufficient resources.
    • The system successfully protects user privacy by preventing the leakage of optimization problem details.
    • PEGA demonstrates comparable performance to conventional genetic algorithms in approximating optimal solutions.

    Conclusions:

    • PEGA offers a viable solution for privacy-preserving cloud-based optimization.
    • The ECaaS paradigm facilitates access to advanced computational capabilities while ensuring data confidentiality.
    • Experimental results validate the effectiveness and security of PEGA for TSP and KP.