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 for SMPC beyond small algorithm sizes, with efficient SMPC implementations of state-of-the-art neural network algorithms currently under active development.
“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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Privacy-Preserving Deep Learning on Machine Learning as a Service—a Comprehensive Survey
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Function Secret Sharing for Mixed-Mode and Fixed-Point Secure Computation
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Secure Evaluation of Quantized Neural Networks
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SiRnn: A Math Library for Secure RNN Inference
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Self-citationPrivacy in Deep Learning: A Survey
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Data Anonymization for Pervasive Health Care: Systematic Literature Mapping Study
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BOLT: Privacy-Preserving, Accurate and Efficient Inference for Transformers
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Application of IOT and machine learning in crop protection against animal intrusion
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Securely Outsourcing Neural Network Inference to the Cloud With Lightweight Techniques
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ZKML: An Optimizing System for ML Inference in Zero-Knowledge Proofs
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GALA: Greedy ComputAtion for Linear Algebra in Privacy-Preserved Neural Networks
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Analog Lagrange Coded Computing
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Privacy-Preserving Federated Learning of Remote Sensing Image Classification With Dishonest Majority
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pvCNN: Privacy-Preserving and Verifiable Convolutional Neural Network Testing
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FLGUARD: Secure and Private Federated Learning
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Privacy-Preserving Deep Learning Based on Multiparty Secure Computation: A Survey
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SecFloat: Accurate Floating-Point meets Secure 2-Party Computation
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Self-citationSecGNN: Privacy-Preserving Graph Neural Network Training and Inference as a Cloud Service
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Orca: FSS-based Secure Training and Inference with GPUs
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Self-citationLLAMA: A Low Latency Math Library for Secure Inference
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Toward Verifiable and Privacy Preserving Machine Learning Prediction
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SoK: Cryptographic Neural-Network Computation
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Side channel attacks for architecture extraction of neural networks
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Pika: Secure Computation using Function Secret Sharing over Rings
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Secure Decentralized Image Classification With Multiparty Homomorphic Encryption
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Fast Privacy-Preserving Text Classification Based on Secure Multiparty Computation
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Enabling Homomorphically Encrypted Inference for Large DNN Models
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Privformer: Privacy-preserving Transformer with MPC
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SoK: Efficient Privacy-preserving Clustering
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Privacy-Preserving Feature Selection with Secure Multiparty ComputationPreprint
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COINN: Crypto/ML Codesign for Oblivious Inference via Neural Networks
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REDsec: Running Encrypted Discretized Neural Networks in Seconds
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Adam in Private: Secure and Fast Training of Deep Neural Networks with Adaptive Moment Estimation
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FastSecNet: An Efficient Cryptographic Framework for Private Neural Network Inference
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Falcon: Honest-Majority Maliciously Secure Framework for Private Deep Learning
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Make Revocation Cheaper: Hardware-Based Revocable Attribute-Based Encryption
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Fairness in the Eyes of the Data: Certifying Machine-Learning Models
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A comprehensive survey and taxonomy on privacy-preserving deep learning
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Characterization of MPC-based Private Inference for Transformer-based Models
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Hercules: Boosting the Performance of Privacy-Preserving Federated Learning
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Fusion: Efficient and Secure Inference Resilient to Malicious Servers
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PUMA: Secure inference of LLaMA-7B in five minutes
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Banners: Binarized Neural Networks with Replicated Secret Sharing
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Masking Feedforward Neural Networks Against Power Analysis Attacks
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Characterizing and Optimizing End-to-End Systems for Private Inference
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Towards secure and practical machine learning via secret sharing and random permutation
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Verifiable Data Mining Against Malicious Adversaries in Industrial Internet of Things
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FlexBNN: Fast Private Binary Neural Network Inference With Flexible Bit-Width
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PVFL: Verifiable federated learning and prediction with privacy-preserving
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MPCDiff: Testing and Repairing MPC-Hardened Deep Learning Models
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AriaNN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing
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Swift: Fast Secure Neural Network Inference With Fully Homomorphic Encryption
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CryptGPU: Fast Privacy-Preserving Machine Learning on the GPUPreprint
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PpNNT: Multiparty Privacy-Preserving Neural Network Training System
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GPU-based Private Information Retrieval for On-Device Machine Learning Inference
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GuardianML: Anatomy of Privacy-Preserving Machine Learning Techniques and Frameworks
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Manto: A Practical and Secure Inference Service of Convolutional Neural Networks for IoT
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LEGO: A hybrid toolkit for efficient 2PC-based privacy-preserving machine learning
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Analog Secret Sharing With Applications to Private Distributed Learning
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SecureTVM: A TVM-based Compiler Framework for Selective Privacy-preserving Neural Inference
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A Secure and Fair Double Auction Framework for Cloud Virtual Machines
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Secure Multi-Party Computation for Personalized Human Activity Recognition
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Smart Crop Protection Against Animal Encroachment using Deep Learning
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Secure Convolution Neural Network Inference Based on Homomorphic Encryption
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Enc2: Privacy-Preserving Inference for Tiny IoTs via Encoding and Encryption
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Privacy-Preserving Video Classification with Convolutional Neural NetworksPreprint
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Surveying Quantum-Proof Blockchain Security: The Era of Exotic Signatures
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Efficient Privacy-Preserving Machine Learning with Lightweight Trusted Hardware
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- 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 › Artificial Intelligence › Cryptography and Data Security
- Physical Sciences › Computer Science › Artificial Intelligence › 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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