In distributed optimization, multiple parties collaborate to find an optimal solution to a problem. Privacy-preserving distributed optimization uses techniques, such as secure multi-party computation (MPC), to protect the private inputs of each party. In time-critical settings, the runtime overhead introduced by privacy-preserving computations may prevent the optimization from finishing within the deadline. This paper presents an approach for privacy-preserving distributed optimization in time-critical settings that combines evolutionary algorithms for solution search and MPC for the evaluation of solutions. The approach reduces the impact of privacy-preserving computations on runtime and allows to return solution within the deadline. Obfuscation of evaluation results provides additional protection for private inputs from an honest-but-curious platform provider, but introduces a potential trade-off between protection and solution quality. This trade-off is investigated in experiments using a genetic algorithm for both the single-objective assignment problem and the traveling salesperson problem, as well as NSGA-II for the multi-objective assignment problem.

翻译：在分布式优化中，多方协作以寻求问题的最优解。隐私保护分布式优化采用安全多方计算（MPC）等技术保护各方的私有输入。在时间敏感场景中，隐私保护计算带来的运行时开销可能使优化无法在截止时间内完成。本文提出一种面向时间敏感场景的隐私保护分布式优化方法，该方法结合了基于进化算法的解搜索与基于MPC的解评估。该方案通过降低隐私保护计算对运行时的影响，使得能在截止时间内返回解。对评估结果的混淆处理为诚实但好奇的平台供应商提供了对私有输入的额外保护，但引入了保护力度与解质量之间的潜在权衡。本文通过实验研究了该权衡：采用遗传算法求解单目标分配问题和旅行商问题，并采用NSGA-II求解多目标分配问题。