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)得到了机器学习、分布式计算和大数据技术的推广,从而推进了EC的新的研究方向,如分布式EC和辅助EC。这些进展显著提高了EC的性能和应用范围,但也引发了隐私泄露,例如最优结果和替代模型的泄漏。因此,进化计算与隐私保护成为了一个新兴研究课题。然而,进化计算中的隐私问题缺乏系统的探究,特别是对隐私保护的对象、动机、位置和方法的探讨。因此,在本文中,我们讨论了三种典型的优化范式(即 \textit{集中式优化、分布式优化和数据驱动优化)来描述进化计算的优化模式,提出了BOOM来梳理进化计算中的隐私问题。具体来说,集中式优化范式允许客户将优化问题外包给集中式服务器,并从服务器获取优化解决方案。分布式优化范式利用分布式设备的存储和计算能力来解决优化问题。此外,数据驱动的优化范式利用历史上收集的数据来解决缺乏明确目标函数的优化问题。本文采用BOOM来描述三种典型优化范式中隐私保护的对象和动机,并讨论在三种典型优化范式中平衡优化性能和隐私保证的潜在隐私保护技术。此外,本文试图预见一些新的隐私保护进化计算的研究方向。