论文标题
使用机密计算的机器学习：知识的系统化
Machine Learning with Confidential Computing: A Systematization of Knowledge
论文作者
论文摘要
机器学习（ML）中的隐私和安全挑战变得越来越严重，随着ML的普遍发展以及最近对大型攻击表面的展示。作为一种成熟的以系统为导向的方法，在各种ML场景中，都在学术界和行业中都使用了机密计算来减轻隐私和安全问题。在本文中，研究了ML和机密计算之间的连词。我们将提供机密计算辅助的ML技术的先前工作系统化，这些技术提供了i）保密保证和II）完整性保证，并讨论其高级功能和缺点。进一步确定了关键挑战，我们提供有关ML用例现有可信赖的执行环境（TEE）系统中限制的专门分析。最后，讨论了潜在的作品，包括针对闭环保护的基础隐私定义，有效的ML执行，用于ML的TEE TEE辅助设计，Tee-Aware ML和ML Full Pipeline保证。通过在我们的知识系统化中提供这些潜在的解决方案，我们旨在建造桥梁，以帮助实现更强大的TEE启用ML，以提供隐私保证，而无需引入计算和系统成本。
Privacy and security challenges in Machine Learning (ML) have become increasingly severe, along with ML's pervasive development and the recent demonstration of large attack surfaces. As a mature system-oriented approach, Confidential Computing has been utilized in both academia and industry to mitigate privacy and security issues in various ML scenarios. In this paper, the conjunction between ML and Confidential Computing is investigated. We systematize the prior work on Confidential Computing-assisted ML techniques that provide i) confidentiality guarantees and ii) integrity assurances, and discuss their advanced features and drawbacks. Key challenges are further identified, and we provide dedicated analyses of the limitations in existing Trusted Execution Environment (TEE) systems for ML use cases. Finally, prospective works are discussed, including grounded privacy definitions for closed-loop protection, partitioned executions of efficient ML, dedicated TEE-assisted designs for ML, TEE-aware ML, and ML full pipeline guarantees. By providing these potential solutions in our systematization of knowledge, we aim to build the bridge to help achieve a much stronger TEE-enabled ML for privacy guarantees without introducing computation and system costs.
