Awesome-Crypto-DNN

• Seesaw: Compensating for Nonlinear Reduction with Linear Computations for Private Inference, Fabing Li, Yuanhao Zhai, Shuangyu Cai, Mingyu Gao, ICML2024, code
• MPCViT: Searching for Accurate and Efficient MPC-Friendly Vision Transformer with Heterogeneous Attention, Wenxuan Zeng, Meng Li, Wenjie Xiong, Tong Tong, Wen-jie Lu, Jin Tan, Runsheng Wang, Ru Huang, ICCV2023
• Learning to Linearize Deep Neural Networks for Secure and Efficient Private Inference, Souvik Kundu, Shunlin Lu, Yuke Zhang, Jacqueline Tiffany Liu, Peter A. Beerel, ICLR2023
• LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inference, Hongwu Peng, Ran Ran, Yukui Luo, Jiahui Zhao, Shaoyi Huang, Kiran Thorat, Tong Geng, Chenghong Wang, Xiaolin Xu, Wujie Wen, Caiwen Ding, NeurIPS2023, code
• AutoReP: Automatic ReLU Replacement for Fast Private Network Inference, Hongwu Peng, Shaoyi Huang, Tong Zhou, Yukui Luo, Chenghong Wang, Zigeng Wang, Jiahui Zhao, Xi Xie, Ang Li, Tony Geng, Kaleel Mahmood, Wujie Wen, Xiaolin Xu, Caiwen Ding, ICCV2023, code
• SAL-ViT: Towards Latency Efficient Private Inference on ViT using Selective Attention Search with a Learnable Softmax Approximation, Yuke Zhang, Dake Chen, Souvik Kundu, Chenghao Li, Peter A. Beerel, ICCV2023
• Selective Network Linearization for Efficient Private Inference, Minsu Cho, Ameya Joshi, Siddharth Garg, Brandon Reagen, Chinmay Hegde, ICML2022, code
• CryptoNAS: Private Inference on a ReLU Budget, Zahra Ghodsi, Akshaj Veldanda, Brandon Reagen, Siddharth Garg, NeurIPS2020, code
• DeepReDuce: ReLU Reduction for Fast Private Inference, Nandan Kumar Jha, Zahra Ghodsi, Siddharth Garg, and Brandon Reagen, ICML2021

2024

• Orca: FSS-based Secure Training and Inference with GPUs, Neha Jawalkar, Kanav Gupta, Arkaprava Basu, Nishanth Chandran, Divya Gupta, Rahul Sharma, SP2024
• BOLT: Privacy-Preserving, Accurate and Efficient Inference for Transformers, Qi Pang, Jinhao Zhu, Helen Möllering, Wenting Zheng, Thomas Schneider, SP2024, code
• Scalable Multi-Party Computation Protocols for Machine Learning in the Honest-Majority Setting, Fengrun Liu, Xiang Xie, Yu Yu, USENIX2024, code
• Fast and Private Inference of Deep Neural Networks by Co-designing Activation Functions, Abdulrahman Diaa, Lucas Fenaux, Thomas Humphries, Marian Dietz, Faezeh Ebrahimianghazani, Bailey Kacsmar, Xinda Li, Nils Lukas, Rasoul Akhavan Mahdavi, Simon Oya, Ehsan Amjadian, Florian Kerschbaum, USENIX2024, code
• AutoFHE: Automated Adaption of CNNs for Efficient Evaluation over FHE, Wei Ao, Vishnu Naresh Boddeti, USENIX2024, code
• Privacy-Preserving Embedding via Look-up Table Evaluation with Fully Homomorphic Encryption, Jae-Yun Kim, Saerom Park, Joohee Lee, Jung Hee Cheon, ICML2024
• Ditto: Quantization-aware Secure Inference of Transformers upon MPC, Haoqi Wu, Wenjing Fang, Yancheng Zheng, Junming Ma, Jin Tan, Lei Wang, ICML2024, code
• Converting Transformers to Polynomial Form for Secure Inference Over Homomorphic Encryption, Itamar Zimerman, Moran Baruch, Nir Drucker, Gilad Ezov, Omri Soceanu, Lior Wolf, ICML2024, code
• CipherDM: Secure Three-Party Inference for Diffusion Model Sampling, Xin Zhao, Xiaojun Chen, Xudong Chen, He Li, Tingyu Fan, Zhendong Zhao, ECCV2024, code
• Sigma: Secure GPT Inference with Function Secret Sharing, Kanav Gupta, Neha Jawalkar, Ananta Mukherjee, Nishanth Chandran, Divya Gupta, Ashish Panwar, Rahul Sharma, PETS2024, code
• NeuJeans: Private Neural Network Inference with Joint Optimization of Convolution and FHE Bootstrapping, Jae Hyung Ju, Jaiyoung Park, Jongmin Kim, Minsik Kang, Donghwan Kim, Jung Hee Cheon, Jung Ho Ahn, CCS2024

2023

• HETAL: Efficient Privacy-preserving Transfer Learning with Homomorphic Encryption, Seewoo Lee, Garam Lee, Jung Woo Kim, Junbum Shin, Mun-Kyu Lee, ICML2023, code
• Penguin: Parallel-Packed Homomorphic Encryption for Fast Graph Convolutional Network Inference, Ran Ran, Nuo Xu, Tao Liu, Wei Wang, Gang Quan, Wujie Wen, NeurIPS2023
• CoPriv: Network/Protocol Co-Optimization for Communication-Efficient Private Inference, Wenxuan Zeng, Meng Li, Haichuan Yang, Wen-jie Lu, Runsheng Wang, Ru Huang, NeurIPS2023
• Convolutions in Overdrive: Maliciously Secure Convolutions for MPC, Marc Rivinius, Pascal Reisert, Sebastian Hasler, and Ralf Küsters, PETS2023
• HeLayers: A Tile Tensors Framework for Large Neural Networks on Encrypted Data, Ehud Aharoni, Allon Adir, Moran Baruch, Nir Drucker, Gilad Ezov, Ariel Farkash, Lev Greenberg, Ramy Masalha, Guy Moshkowich, Dov Murik, Hayim Shaul, and Omri Soceanu, PETS2023, code
• Multi-Party Replicated Secret Sharing over a Ring with Applications to Privacy-Preserving Machine Learning, Alessandro Baccarini, Marina Blanton, Chen Yuan, PETS2023
• HELiKs: HE Linear Algebra Kernels for Secure Inference, Shashank Balla, Farinaz Koushanfar, CCS2023, code
• SoK: Cryptographic Neural-Network Computation, Lucien K. L. Ng, Sherman S. M. Chow, SP2023
• ShadowNet: A Secure and Efficient On-device Model Inference System for Convolutional Neural Networks, Zhichuang Sun, Ruimin Sun, Changming Liu, Amrita Roy Chowdhury, Long Lu, Somesh Jha, SP2023, code
• Fusion: Efficient and Secure Inference Resilient to Malicious Servers, Caiqin Dong, Jian Weng, Jia-Nan Liu, Yue Zhang, Yao Tong, Anjia Yang, Yudan Cheng, Shun Hu, NDSS2023
• REDsec: Running Encrypted Discretized Neural Networks in Seconds, Lars Folkerts, Charles Gouert, Nektarios Georgios Tsoutsos, NDSS2023
• Secure Floating-Point Training, Deevashwer Rathee, Anwesh Bhattacharya, Divya Gupta, Rahul Sharma, Dawn Song, USENIX2023
• Bicoptor: Two-round Secure Three-party Non-linear Computation without Preprocessing for Privacy-preserving Machine Learning, Lijing Zhou, Ziyu Wang, Hongrui Cui, Qingrui Song, Yu Yu, SP2023

2022

• Low-Complexity Deep Convolutional Neural Networks on Fully Homomorphic Encryption Using Multiplexed Parallel Convolutions, Eunsang Lee, Joon-Woo Lee, Junghyun Lee, Young-Sik Kim, Yongjune Kim, Jong-Seon No, Woosuk Choi, ICML2022, code
• Sphinx: Enabling Privacy-Preserving Online Learning over the Cloud, Han Tian, Chaoliang Zeng, Zhenghang Ren, Di Chai, Junxue ZHANG, Kai Chen, Qiang Yang, SP2022
• SecFloat: Accurate Floating-Point meets Secure 2-Party Computation, Deevashwer Rathee, Anwesh Bhattacharya, Rahul Sharma, Divya Gupta, Nishanth Chandran, Aseem Rastogi, SP2022, code
• AriaNN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing, Théo Ryffel, Pierre Tholoniat, David Pointcheval, Francis R. Bach, PETS2022, code
• Secure Quantized Training for Deep Learning, Marcel Keller, Ke Sun, ICML2022, code
• Iron: Private Inference on Transformers, Meng Hao, Hongwei Li, Hanxiao Chen, Pengzhi Xing, Guowen Xu, Tianwei Zhang, NeurIPS2022, code
• CryptoGCN: Fast and Scalable Homomorphically Encrypted Graph Convolutional Network Inference, Ran Ran, Wei Wang, Quan Gang, Jieming Yin, Nuo Xu, Wujie Wen, NeurIPS2022, code
• 3LegRace: Privacy-Preserving DNN Training over TEEs and GPUs, Yue Niu, Ramy E. Ali, Salman Avestimehr, PETS2022
• Private and Reliable Neural Network Inference, Nikola Jovanovic, Marc Fischer, Samuel Steffen, Martin T. Vechev, CCS2022
• Tetrad: Actively Secure 4PC for Secure Training and Inference, Nishat Koti, Arpita Patra, Rahul Rachuri, Ajith Suresh, NDSS2022
• Piranha: A GPU Platform for Secure Computation, Jean-Luc Watson, Sameer Wagh, Raluca Ada Popa, USENIX2022
• Cheetah: Lean and Fast Secure Two-Party Deep Neural Network Inference, Zhicong Huang, Wen-jie Lu, Cheng Hong, Jiansheng Ding, USENIX2022

2021

• SAFENet: A Secure, Accurate and Fast Neural Network Inference, Qian Lou, Yilin Shen, Hongxia Jin, Lei Jiang, ICLR2021
• CRYPTOGRU: Low Latency Privacy-Preserving Text Analysis With GRU, Bo Feng, Qian Lou, Lei Jiang, Geoffrey Fox, EMNLP2021
• CrypTen: Secure Multi-Party Computation Meets Machine Learning, Brian Knott, Shobha Venkataraman, Awni Hannun, Shubho Sengupta, Mark Ibrahim, Laurens van der Maaten, NeurIPS2021, code
• Circa: Stochastic ReLUs for Private Deep Learning, Zahra Ghodsi, Nandan Kumar Jha, Brandon Reagen, Siddharth Garg, NeurIPS2021
• Falcon: Honest-Majority Maliciously Secure Framework for Private Deep Learning, Sameer Wagh, Shruti Tople, Fabrice Benhamouda, Eyal Kushilevitz, Prateek Mittal, Tal Rabin, PETS2021, code
• GALA: Greedy ComputAtion for Linear Algebra in Privacy-Preserved Neural Networks, Qiao Zhang, Chunsheng Xin, Hongyi Wu, NDSS 2021
• SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning, Nishat Koti, Mahak Pancholi, Arpita Patra, Ajith Suresh, USENIX2021
• Muse: Secure Inference Resilient to Malicious Clients, Ryan Lehmkuhl, Pratyush Mishra, Akshayaram Srinivasan, Raluca Ada Popa, USENIX2021, code
• GForce: GPU-Friendly Oblivious and Rapid Neural Network Inference, Lucien K. L. Ng, Sherman S. M. Chow, USENIX2021, code
• ABY2.0: Improved Mixed-Protocol Secure Two-Party Computation, Arpita Patra, Thomas Schneider, Ajith Suresh, Hossein Yalame, USENIX2021
• Fantastic Four: Honest-Majority Four-Party Secure Computation With Malicious Security, Anders P. K. Dalskov, Daniel Escudero, Marcel Keller, USENIX2021
• CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU, Sijun Tan, Brian Knott, Yuan Tian, David J. Wu, SP2021, code
• Pegasus: Bridging Polynomial and Non-polynomial Evaluations in Homomorphic Encryption, Wen-jie Lu, Zhicong Huang, Cheng Hong, Yiping Ma, Hunter Qu, SP2021, code
• SIRNN: A Math Library for Secure RNN Inference, Deevashwer Rathee, Mayank Rathee, Rahul Kranti Kiran Goli, Divya Gupta, Rahul Sharma, Nishanth Chandran, Aseem Rastogi, SP2021, code

2020

• AutoPrivacy: Automated Layer-wise Parameter Selection for Secure Neural Network Inference, Qian Lou, Song Bian, Lei Jiang, NeurIPS2020
• Falcon: Fast Spectral Inference on Encrypted Data, Qian Lou, Wen-jie Lu, Cheng Hong, Lei Jiang, NeurIPS2020
• Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data, Qian Lou, Bo Feng, Geoffrey Charles Fox, Lei Jiang, NeurIPS2020
• BLAZE: Blazing Fast Privacy-Preserving Machine Learning, Arpita Patra, Ajith Suresh, NDSS2020
• Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning, Harsh Chaudhari, Rahul Rachuri, Ajith Suresh, NDSS2020
• Delphi: A Cryptographic Inference Service for Neural Networks, Pratyush Mishra, Ryan Lehmkuhl, Akshayaram Srinivasan, Wenting Zheng, Raluca Ada Popa, USENIX2020, code
• CrypTFlow : Secure TensorFlow Inference, Nishant Kumar, Mayank Rathee, Nishanth Chandran, Divya Gupta, Aseem Rastogi, Rahul Sharma, SP2020, code
• CrypTFlow2: Practical 2-Party Secure Inference, Deevashwer Rathee, Mayank Rathee, Nishant Kumar, Nishanth Chandran, Divya Gupta, Aseem Rastogi, Rahul Sharma, CCS2020, code
• ENSEI: Efficient Secure Inference via Frequency-Domain Homomorphic Convolution for Privacy-Preserving Visual Recognition, Song Bian, Tianchen Wang, Masayuki Hiromoto, Yiyu Shi, Takashi Sato, CVPR2020
• FALCON: A Fourier Transform Based Approach for Fast and Secure Convolutional Neural Network Predictions, Shaohua Li, Kaiping Xue, Bin Zhu, Chenkai Ding, Xindi Gao, David Wei, Tao Wan, CVPR2020

2019

• EzPC: Programmable and Efficient Secure Two-Party Computation for Machine Learning, Nishanth Chandran, Divya Gupta, Aseem Rastogi, Rahul Sharma, Shardul Tripathi, EuroSP2019, code
• Privacy-Enhanced Machine Learning with Functional Encryption, Tilen Marc, Miha Stopar, Jan Hartman, Manca Bizjak, Jolanda Modic, ESORICS2019, code
• Low Latency Privacy Preserving Inference, Alon Brutzkus, Ran Gilad-Bachrach, Oren Elisha, ICML2019
• SHE: A Fast and Accurate Deep Neural Network for Encrypted Data, Qian Lou, Lei Jiang, NeurIPS2019, code
• Partially Encrypted Deep Learning using Functional Encryption, Théo Ryffel, David Pointcheval, Francis Bach, Edouard Dufour-Sans, Romain Gay, NeurIPS2019
• QUOTIENT: Two-Party Secure Neural Network Training and Prediction, Nitin Agrawal, Ali Shahin Shamsabadi, Matt J. Kusner, Adrià Gascón, CCS2019
• SecureNN: 3-Party Secure Computation for Neural Network Training, Sameer Wagh, Divya Gupta, Nishanth Chandran, PETS2019, code
• Efficient Multi-Key Homomorphic Encryption with Packed Ciphertexts with Application to Oblivious Neural Network Inference, Hao Chen, Wei Dai, Miran Kim, Yongsoo Song, CCS2019
• XONN: XNOR-based Oblivious Deep Neural Network Inference, M. Sadegh Riazi, Mohammad Samragh, Hao Chen, Kim Laine, Kristin E. Lauter, Farinaz Koushanfar, USENIX2019
• Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware, Florian Tramèr, Dan Boneh, ICLR2019, code

2018

• TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service, Amartya Sanyal, Matt Kusner, Adria Gascon, Varun Kanade, ICML2018
• ABY3: A Mixed Protocol Framework for Machine Learning, Payman Mohassel, Peter Rindal, CCS2018, code
• GAZELLE: A Low Latency Framework for Secure Neural Network Inference, Chiraag Juvekar, Vinod Vaikuntanathan, Anantha P. Chandrakasan, USENIX2018, code

2017

• SecureML: A System for Scalable Privacy-Preserving Machine Learning, Payman Mohassel, Yupeng Zhang, SP2017
• Oblivious Neural Network Predictions via MiniONN transformations,Jian Liu, Mika Juuti, Yao Lu, N. Asokan, CCS2017, code

2016

• CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy, Ran Gilad-Bachrach, Nathan Dowlin, Kim Laine, Kristin Lauter, Michael Naehrig, John Wernsing, ICML2016