随着边缘设备和云计算的发展，在过去的十年中，如何在保护隐私和安全的前提下完成机器学习和优化任务受到了越来越多的关注。联邦学习（FL）作为一种保护隐私的分布式机器学习方法，在过去的几年中已经得到了广泛的关注。然而，在优化问题中也会出现数据隐私保护问题，这一点到目前为止还很少受到关注。本文对此进行了研究，重点关注数据驱动的进化优化下的隐私保护问题，旨在通过总结机器学习和优化算法领域共通的安全机制和隐私保护方法，提供一个从安全隐私保护联邦学习到安全隐私保护联邦优化的知识路线图。本文首先对机器学习中的安全和隐私问题进行了明确定义，然后对联邦学习方法和加密隐私保护技术进行了全面的回顾。然后，本文对隐私保护优化这一新兴领域展开了讨论，涵盖了从隐私保护的分布式优化、隐私保护的进化优化以及隐私保护的贝叶斯优化。之后本文进一步从推理攻击和主动攻击的角度，对贝叶斯优化和进化优化方法进行了全面的安全性分析。在此基础上，深入讨论了哪些联邦学习和分布式优化策略可以用于联邦优化的设计，以及应用这些策略需要哪些额外的条件。最后，本文概述了联邦数据驱动优化中的悬而未决的问题和尚存的挑战。本文为联邦学习和联邦优化之间的关联提供了崭新的见解，并提升学术界对安全联邦优化的研究兴趣。

With the development of edge devices and cloud computing, the question of how to accomplish machine learning and optimization tasks in a privacy-preserving and secure way has attracted increased attention over the past decade. As a privacy-preserving distributed machine learning method, federated learning (FL) has become popular in the last few years. However, the data privacy issue also occurs when solving optimization problems, which has received little attention so far. This survey paper is concerned with privacy-preserving optimization, with a focus on privacy-preserving data-driven evolutionary optimization. It aims to provide a roadmap from secure privacy-preserving learning to secure privacy-preserving optimization by summarizing security mechanisms and privacy-preserving approaches that can be employed in machine learning and optimization. We provide a formal definition of security and privacy in learning, followed by a comprehensive review of FL schemes and cryptographic privacy-preserving techniques. Then, we present ideas on the emerging area of privacy-preserving optimization, ranging from privacy-preserving distributed optimization to privacy-preserving evolutionary optimization and privacy-preserving Bayesian optimization (BO). We further provide a thorough security analysis of BO and evolutionary optimization methods from the perspective of inferring attacks and active attacks. On the basis of the above, an in-depth discussion is given to analyze what FL and distributed optimization strategies can be used for the design of federated optimization and what additional requirements are needed for achieving these strategies. Finally, we conclude the survey by outlining open questions and remaining challenges in federated data-driven optimization. We hope this survey can provide insights into the relationship between FL and federated optimization and will promote research interest in secure federated optimization.