CrypTen: Secure Multi-Party Computation Meets Machine Learning

By Brian Knott, Shobha Venkataraman, Awni Y. Hannun, Shubho Sengupta, Mark Ibrahim, L. V. D. van der Maaten. Neural Information Processing Systems, 2021

544 citations recorded by the source corpus.

Secure multi-party computation (MPC) allows parties to perform computations on data while keeping that data private. This capability has great potential for machine-learning applications: it facilitates training of machine-learning models on private data sets owned by different parties, evaluation of one party's private model using another party's private data, etc. Although a range of studies implement machine-learning models via secure MPC, such implementations are not yet mainstream. Adoption of secure MPC is hampered by the absence of flexible software frameworks that"speak the language"of machine-learning researchers and engineers. To foster adoption of secure MPC in machine learning, we present CrypTen: a software framework that exposes popular secure MPC primitives via abstractions that are common in modern machine-learning frameworks, such as tensor computations, automatic differentiation, and modular neural networks. This paper describes the design of CrypTen and measure its performance on state-of-the-art models for text classification, speech recognition, and image classification. Our benchmarks show that CrypTen's GPU support and high-performance communication between (an arbitrary number of) parties allows it to perform efficient private evaluation of modern machine-learning models under a semi-honest threat model. For example, two parties using CrypTen can securely predict phonemes in speech recordings using Wav2Letter faster than real-time. We hope that CrypTen will spur adoption of secure MPC in the machine-learning community.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/7eb733c8ac1b3d1dd8b50e066ddae10769e3b46e

How researchers see this paper

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In Artificial Intelligence

1. Fahad Shahbaz Khan
2. Salman Hameed Khan
3. Bo Li
4. Bowen Zhou
5. Munawar Hayat
6. Jinfeng Yi
7. Huan Fu
8. Shuai Di

Citations in the corpus

• Opacus: User-Friendly Differential Privacy Library in PyTorch (2021, unknown): “Last, we point to some efforts to perform ML training under different notions of privacy and security: the Myelin system makes use of secure hardware to add noise centrally [6], while Crypten [7] does training in a secure multi-party setting, without explicit noise addition.”
• Trustworthy AI: From Principles to Practices (2021, methodology): “Several tools have been open sourced, such as MP2ML [48], CryptoSPN [330], CrypTFlow [200, 276], and CrypTen [188].”
• Transformers in Medical Imaging: A Survey (2022, methodology): “CrypTen [419] PyTorch Framework to facilitate research in secure and privacy-preserving machine learning.”