Timezone: »

CrypTen: Secure Multi-Party Computation Meets Machine Learning
Brian Knott · Shobha Venkataraman · Awni Hannun · Shubho Sengupta · Mark Ibrahim · Laurens van der Maaten

Tue Dec 07 08:30 AM -- 10:00 AM (PST) @

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.

Author Information

Brian Knott (Facebook)
Shobha Venkataraman (Facebook)
Awni Hannun (Facebook)
Shubho Sengupta (Facebook AI Research)
Mark Ibrahim (Facebook AI Research)

Mark Ibrahim is a senior machine learning engineer with a background in mathematics, deep learning, and knowledge graphs. He has worked on methods to interpret neural network predictions and applications of deep learning to forecasting. He enjoys good coffee, eating well, and editing text in Vim.

Laurens van der Maaten (Facebook)

More from the Same Authors