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CrypTFlow: Secure TensorFlow Inference
Open accessAbstract
We present CrypTFlow, a first of its kind system that converts TensorFlow inference code into Secure Multi-party Computation (MPC) protocols at the push of a button. To do this, we build three components. Our first component, Athos, is an end-to-end compiler from TensorFlow to a variety of semihonest MPC protocols. The second component, Porthos, is an improved semi-honest 3-party protocol that provides significant speedups for TensorFlow like applications. Finally, to provide malicious secure MPC protocols, our third component, Aramis, is a novel technique that uses hardware with integrity guarantees to convert any semi-honest MPC protocol into an MPC protocol that provides malicious security. The malicious security of the protocols output by Aramis relies on integrity of the hardware and semi-honest security of MPC. Moreover, our system matches the inference accuracy of plaintext TensorFlow.We experimentally demonstrate the power of our system by showing the secure inference of real-world neural networks such as ResNet50 and DenseNet121 over the ImageNet dataset with running times of about 30 seconds for semi-honest security and under two minutes for malicious security. Prior work in the area of secure inference has been limited to semi-honest security of small networks over tiny datasets such as MNIST or CIFAR. Even on MNIST/CIFAR, CrypTFlow outperforms prior work.
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“computational considerations are a concern beyond small algorithm sizes, with efficient SMPC implementations of state-of-the-art neural network algorithms currently under active development 78 .”
From Secure, privacy-preserving and federated machine learning in medical imaging (2020) · cites this paper as [78]
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Analog Secret Sharing With Applications to Private Distributed Learning
Soleymani, M., Mahdavifar, H., & Avestimehr, A. S. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3173417 · Cited by 12
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Metadata
- Authors
- Kumar, N., Rathee, M., Chandran, N., Gupta, D., Rastogi, A., & Sharma, R.
- Affiliations
- Microsoft (United States) (US)
- Venue
- 2020 IEEE Symposium on Security and Privacy (SP)
- Publisher
- IEEE
- Published
- 2020-05
- Volume
- 2019
- Pages
- 336-353
- ISSN
- 1081-6011 (ISSN-L)
- OpenAlex
- W2974824775
- Semantic Scholar
- 5a2a7bcb24488a4ee938e376cc876d7c2fe115a7
- Cited by
- 245
- References
- 85
- Citations by year
- 2026: 18 · 2025: 41 · 2024: 48 · 2023: 43 · 2022: 33 · 2021: 42 · 2020: 20
- FWCI
- 13.18
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- IEEE Symposium on Security and Privacy (S&P) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- IACR Cryptology ePrint Archive (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Cryptography and Data Security
- Physical Sciences › Computer Science › Hardware and Architecture › Security and Verification in Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- secure multi-party computation, secure inference, TensorFlow, neural network inference, semi-honest security, malicious security, compilers, hardware integrity, secure machine learning, ResNet50, DenseNet121, ImageNet
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