Machine learning (ML) is increasingly being deployed in critical systems. The data dependence of ML makes securing data used to train and test ML-enabled systems of utmost importance. While the field of cybersecurity has well-established practices for securing information, ML-enabled systems create new attack vectors. Furthermore, data science and cybersecurity domains adhere to their own set of skills and terminologies. This survey aims to present background information for experts in both domains in topics such as cryptography, access control, zero trust architectures, homomorphic encryption, differential privacy for machine learning, and federated learning to establish shared foundations and promote advancements in data security.

翻译：机器学习（ML）正越来越多地部署于关键系统中。ML对数据的依赖性使得保护用于训练和测试ML驱动系统的数据至关重要。尽管网络安全领域在信息保护方面已形成成熟实践，但ML驱动系统引入了新型攻击向量。此外，数据科学与网络安全领域各自遵循特定的技能体系与术语规范。本综述旨在为两个领域的专家提供密码学、访问控制、零信任架构、同态加密、面向机器学习的差分隐私以及联邦学习等主题的背景知识，以建立共同基础并促进数据安全领域的进步。