Recently, evolutionary computation (EC) has been promoted by machine learning, distributed computing, and big data technologies, resulting in new research directions of EC like distributed EC and surrogate-assisted EC. These advances have significantly improved the performance and the application scope of EC, but also trigger privacy leakages, such as the leakage of optimal results and surrogate model. Accordingly, evolutionary computation combined with privacy protection is becoming an emerging topic. However, privacy concerns in evolutionary computation lack a systematic exploration, especially for the object, motivation, position, and method of privacy protection. To this end, in this paper, we discuss three typical optimization paradigms (i.e., \textit{centralized optimization, distributed optimization, and data-driven optimization}) to characterize optimization modes of evolutionary computation and propose BOOM to sort out privacy concerns in evolutionary computation. Specifically, the centralized optimization paradigm allows clients to outsource optimization problems to the centralized server and obtain optimization solutions from the server. While the distributed optimization paradigm exploits the storage and computational power of distributed devices to solve optimization problems. Also, the data-driven optimization paradigm utilizes data collected in history to tackle optimization problems lacking explicit objective functions. Particularly, this paper adopts BOOM to characterize the object and motivation of privacy protection in three typical optimization paradigms and discusses potential privacy-preserving technologies balancing optimization performance and privacy guarantees in three typical optimization paradigms. Furthermore, this paper attempts to foresee some new research directions of privacy-preserving evolutionary computation.

翻译：近年来，进化计算（EC）受机器学习、分布式计算和大数据技术的推动，催生了分布式进化计算与替代模型辅助进化计算等新研究方向。这些进展显著提升了进化计算的性能与应用范围，但也引发了隐私泄露问题，例如最优结果与替代模型的泄露。因此，结合隐私保护的进化计算正成为新兴研究热点。然而，进化计算中的隐私问题缺乏系统性探索，尤其关于隐私保护的对象、动机、定位与方法。为此，本文讨论了三种典型优化范式（即集中式优化、分布式优化和数据驱动优化）以刻画进化计算的优化模式，并提出BOOM框架来梳理进化计算中的隐私问题。具体而言，集中式优化范式允许客户端将优化问题外包至集中式服务器并获取优化解；分布式优化范式利用分布式设备的存储与计算能力解决优化问题；数据驱动优化范式则利用历史收集的数据处理缺乏显式目标函数的优化问题。本文特别采用BOOM框架刻画三种典型优化范式中隐私保护的对象与动机，并探讨三种典型优化范式中平衡优化性能与隐私保护能力的潜在隐私保护技术。此外，本文尝试展望隐私保护进化计算的若干新研究方向。