Recent advancements in privacy-preserving machine learning are paving the way to extend the benefits of ML to highly sensitive data that, until now, have been hard to utilize due to privacy concerns and regulatory constraints. Simultaneously, there is a growing emphasis on enhancing the transparency and accountability of machine learning, including the ability to audit ML deployments. While ML auditing and PPML have both been the subjects of intensive research, they have predominately been examined in isolation. However, their combination is becoming increasingly important. In this work, we introduce Arc, an MPC framework for auditing privacy-preserving machine learning. At the core of our framework is a new protocol for efficiently verifying MPC inputs against succinct commitments at scale. We evaluate the performance of our framework when instantiated with our consistency protocol and compare it to hashing-based and homomorphic-commitment-based approaches, demonstrating that it is up to 10^4x faster and up to 10^6x more concise.

翻译：近期隐私保护机器学习的进展正将机器学习的益处扩展到高度敏感数据，这些数据此前因隐私顾虑和监管限制而难以利用。与此同时，提升机器学习透明度和问责制的呼声日益高涨，包括审计机器学习部署的能力。尽管机器学习审计和隐私保护机器学习均已被广泛研究，但两者主要被孤立审视。然而，它们的结合正变得愈发重要。在本工作中，我们提出了Arc——一个用于审计隐私保护机器学习的多方计算框架。该框架的核心是一个新型协议，能够高效验证多方计算输入与简洁承诺的一致性，且具备可扩展性。我们评估了基于一致性协议实例化的框架性能，并与基于哈希和同态承诺的方法进行对比，结果表明其速度提升高达10^4倍，简洁性提升高达10^6倍。