# CrypTFlow2: Practical 2-Party Secure Inference

arXiv: https://arxiv.org/abs/2010.06457 · HTML: https://mlsanity.com/paper/2010.06457/

## Key facts

- Cited by 82 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2020.

## Built on by (most cited first, 30 of 82)

- [Trustworthy AI: From Principles to Practices](https://mlsanity.com/paper/2110.01167/) (2021)
- [Selective Network Linearization for Efficient Private Inference](https://mlsanity.com/paper/2202.02340/) (2022)
- [SIRNN: A Math Library for Secure RNN Inference](https://mlsanity.com/paper/2105.04236/) (2021)
- [A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy](https://mlsanity.com/paper/2302.10637/) (2023)
- [TRUCE: Private Benchmarking to Prevent Contamination and Improve Comparative Evaluation of LLMs](https://mlsanity.com/paper/2403.00393/) (2024)
- [LLMs Can Understand Encrypted Prompt: Towards Privacy-Computing Friendly Transformers](https://mlsanity.com/paper/2305.18396/) (2023)
- [MPCViT: Searching for Accurate and Efficient MPC-Friendly Vision Transformer with Heterogeneous Attention](https://mlsanity.com/paper/2211.13955/) (2022)
- [Bytes Are All You Need: Transformers Operating Directly On File Bytes](https://mlsanity.com/paper/2306.00238/) (2023)
- [Nimbus: Secure and Efficient Two-Party Inference for Transformers](https://mlsanity.com/paper/2411.15707/) (2024)
- [Secure Quantized Training for Deep Learning](https://mlsanity.com/paper/2107.00501/) (2021)
- [Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep Learning](https://mlsanity.com/paper/2107.12342/) (2021)
- [SLIP: Securing LLMs IP Using Weights Decomposition](https://mlsanity.com/paper/2407.10886/) (2024)
- [Split HE: Fast Secure Inference Combining Split Learning and Homomorphic Encryption](https://mlsanity.com/paper/2202.13351/) (2022)
- [SecGNN: Privacy-Preserving Graph Neural Network Training and Inference as a Cloud Service](https://mlsanity.com/paper/2202.07835/) (2022)
- [CipherPrune: Efficient and Scalable Private Transformer Inference](https://mlsanity.com/paper/2502.16782/) (2025)
- [HyPHEN: A Hybrid Packing Method and Optimizations for Homomorphic Encryption-Based Neural Networks](https://mlsanity.com/paper/2302.02407/) (2023)
- [East: Efficient and Accurate Secure Transformer Framework for Inference](https://mlsanity.com/paper/2308.09923/) (2023)
- [EmojiPrompt: Generative Prompt Obfuscation for Privacy-Preserving Communication with Cloud-based LLMs](https://mlsanity.com/paper/2402.05868/) (2024)
- [A Practical Cross-Device Federated Learning Framework over 5G Networks](https://mlsanity.com/paper/2204.08134/) (2022)
- [Comet: A Communication-efficient and Performant Approximation for Private Transformer Inference](https://mlsanity.com/paper/2405.17485/) (2024)
- [Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized Devices](https://mlsanity.com/paper/2303.00492/) (2023)
- [NeuJeans: Private Neural Network Inference with Joint Optimization of Convolution and FHE Bootstrapping](https://mlsanity.com/paper/2312.04356/) (2023)
- [MPC-Minimized Secure LLM Inference](https://mlsanity.com/paper/2408.03561/) (2024)
- [Unsupervised Information Obfuscation for Split Inference of Neural Networks](https://mlsanity.com/paper/2104.11413/) (2021)
- [SafeNet: The Unreasonable Effectiveness of Ensembles in Private Collaborative Learning](https://mlsanity.com/paper/2205.09986/) (2022)
- [Training Large Scale Polynomial CNNs for E2E Inference over Homomorphic Encryption](https://mlsanity.com/paper/2304.14836/) (2023)
- [Falcon: Accelerating Homomorphically Encrypted Convolutions for Efficient Private Mobile Network Inference](https://mlsanity.com/paper/2308.13189/) (2023)
- [GAN-based Domain Inference Attack](https://mlsanity.com/paper/2212.11810/) (2022)
- [Protecting Split Learning by Potential Energy Loss](https://mlsanity.com/paper/2210.09617/) (2022)
- [CipherDM: Secure Three-Party Inference for Diffusion Model Sampling](https://mlsanity.com/paper/2409.05414/) (2024)

## Abstract (arXiv)

We present CrypTFlow2, a cryptographic framework for secure inference over realistic Deep Neural Networks (DNNs) using secure 2-party computation. CrypTFlow2 protocols are both correct -- i.e., their outputs are bitwise equivalent to the cleartext execution -- and efficient -- they outperform the state-of-the-art protocols in both latency and scale. At the core of CrypTFlow2, we have new 2PC protocols for secure comparison and division, designed carefully to balance round and communication complexity for secure inference tasks. Using CrypTFlow2, we present the first secure inference over ImageNet-scale DNNs like ResNet50 and DenseNet121. These DNNs are at least an order of magnitude larger than those considered in the prior work of 2-party DNN inference. Even on the benchmarks considered by prior work, CrypTFlow2 requires an order of magnitude less communication and 20x-30x less time than the state-of-the-art.