Confidential computing has gained prominence due to the escalating volume of data-driven applications (e.g., machine learning and big data) and the acute desire for secure processing of sensitive data, particularly, across distributed environments, such as edge-to-cloud continuum. Provided that the works accomplished in this emerging area are scattered across various research fields, this paper aims at surveying the fundamental concepts, and cutting-edge software and hardware solutions developed for confidential computing using trusted execution environments, homomorphic encryption, and secure enclaves. We underscore the significance of building trust in both hardware and software levels and delve into their applications particularly for machine learning (ML) applications. While substantial progress has been made, there are some barely-explored areas that need extra attention from the researchers and practitioners in the community to improve confidentiality aspects, develop more robust attestation mechanisms, and to address vulnerabilities of the existing trusted execution environments. Providing a comprehensive taxonomy of the confidential computing landscape, this survey enables researchers to advance this field to ultimately ensure the secure processing of users' sensitive data across a multitude of applications and computing tiers.

翻译：机密计算因数据驱动应用（如机器学习与大数据）规模的激增以及对敏感数据安全处理的迫切需求而日益受到重视，尤其在边缘到云连续体这类分布式环境中。鉴于该新兴领域现有研究成果分散于不同学科，本文旨在系统梳理其基础概念，以及基于可信执行环境、同态加密和安全飞地所开发的尖端软硬件解决方案。我们强调在硬件与软件层面建立信任的重要性，并深入探讨其特别是机器学习（ML）领域的应用。尽管已取得显著进展，仍存在一些鲜少探索的领域，需学术界与工业界研究者额外关注，以强化保密性机制、开发更稳健的远程验证方法，并应对现有可信执行环境的漏洞。通过提供机密计算领域的综合性分类体系，本综述将助力研究者推进该领域发展，最终确保用户敏感数据在多种应用与计算层级中的安全处理。