论文标题
不受信任平台上的机密机器学习：调查
Confidential Machine Learning on Untrusted Platforms: A Survey
论文作者
论文摘要
鉴于不断增长的数据以及开发强大的机器学习模型的需求，数据所有者越来越依赖于各种不信任的平台（例如，公共云，边缘，边缘和机器学习服务提供商）来进行可扩展的处理或协作学习。因此，敏感的数据和模型有未经授权的访问，滥用和隐私妥协的危险。一个相对较新的研究机构秘密地培训机器学习模型，以解决这些问题。在这项调查中，我们总结了这一新兴研究领域的显着研究。通过一个统一的框架，我们强调了将机器学习秘密学习的关键挑战和创新。我们专注于机密机器学习（CML）的加密方法，主要用于模型培训，同时还涵盖了其他方向，例如基于扰动的方法和硬件辅助计算环境中的CML。讨论将采用一种全面的方式来考虑相关威胁模型，安全假设，设计原则以及相关权衡的富裕背景，以及数据实用程序，成本和机密性。
With the ever-growing data and the need for developing powerful machine learning models, data owners increasingly depend on various untrusted platforms (e.g., public clouds, edges, and machine learning service providers) for scalable processing or collaborative learning. Thus, sensitive data and models are in danger of unauthorized access, misuse, and privacy compromises. A relatively new body of research confidentially trains machine learning models on protected data to address these concerns. In this survey, we summarize notable studies in this emerging area of research. With a unified framework, we highlight the critical challenges and innovations in outsourcing machine learning confidentially. We focus on the cryptographic approaches for confidential machine learning (CML), primarily on model training, while also covering other directions such as perturbation-based approaches and CML in the hardware-assisted computing environment. The discussion will take a holistic way to consider a rich context of the related threat models, security assumptions, design principles, and associated trade-offs amongst data utility, cost, and confidentiality.
