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.

本文介绍了 BOOM 算法，研究了进化计算中的隐私保护问题。针对三种典型的优化范式（即集中式、分布式和数据驱动的优化），本文采用 BOOM 算法以平衡优化性能和隐私保证的方式探讨了潜在的隐私保护技术并提出新的隐私保护进化计算方向。