- 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
- 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
- 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
- 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
- Muse: Secure Inference Resilient to Malicious Clients, Ryan Lehmkuhl, Pratyush Mishra, Akshayaram Srinivasan, Raluca Ada Popa, USENIX2021, code
- SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning, Nishat Koti, Mahak Pancholi, Arpita Patra, Ajith Suresh, USENIX2021
- Delphi: A Cryptographic Inference Service for Neural Networks, Pratyush Mishra, Ryan Lehmkuhl, Akshayaram Srinivasan, Wenting Zheng, Raluca Ada Popa, USENIX2020, code
- GALA: Greedy ComputAtion for Linear Algebra in Privacy-Preserved Neural Networks, Qiao Zhang, Chunsheng Xin, Hongyi Wu, NDSS 2021
- Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning, Harsh Chaudhari, Rahul Rachuri, Ajith Suresh, NDSS2020
- BLAZE: Blazing Fast Privacy-Preserving Machine Learning, Arpita Patra, Ajith Suresh, NDSS2020
- XONN: XNOR-based Oblivious Deep Neural Network Inference, M. Sadegh Riazi, Mohammad Samragh, Hao Chen, Kim Laine, Kristin E. Lauter, Farinaz Koushanfar, USENIX2019
- GAZELLE: A Low Latency Framework for Secure Neural Network Inference, Chiraag Juvekar, Vinod Vaikuntanathan, Anantha P. Chandrakasan, USENIX2018, code
- Oblivious Neural Network Predictions via MiniONN transformations,Jian Liu, Mika Juuti, Yao Lu, N. Asokan, CCS2017, code
- ABY3: A Mixed Protocol Framework for Machine Learning, Payman Mohassel, Peter Rindal, CCS2018, 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
- QUOTIENT: Two-Party Secure Neural Network Training and Prediction, Nitin Agrawal, Ali Shahin Shamsabadi, Matt J. Kusner, Adrià Gascón, CCS2019
- SecureML: A System for Scalable Privacy-Preserving Machine Learning, Payman Mohassel, Yupeng Zhang, SP2017
- AriaNN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing, Théo Ryffel, Pierre Tholoniat, David Pointcheval, Francis R. Bach, PETS2022, code
- SecureNN: 3-Party Secure Computation for Neural Network Training, Sameer Wagh, Divya Gupta, Nishanth Chandran, PETS2019, code
- Falcon: Honest-Majority Maliciously Secure Framework for Private Deep Learning, Sameer Wagh, Shruti Tople, Fabrice Benhamouda, Eyal Kushilevitz, Prateek Mittal, Tal Rabin, PETS2021, code
- CrypTen: Secure Multi-Party Computation Meets Machine Learning, Brian Knott, Shobha Venkataraman, Awni Hannun, Shubho Sengupta, Mark Ibrahim, Laurens van der Maaten, NeurIPS2021, code
- Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data, Qian Lou, Bo Feng, Geoffrey Charles Fox, Lei Jiang, NeurIPS2020
- Falcon: Fast Spectral Inference on Encrypted Data, Qian Lou, Wen-jie Lu, Cheng Hong, Lei Jiang, NeurIPS2020
- AutoPrivacy: Automated Layer-wise Parameter Selection for Secure Neural Network Inference, Qian Lou, Song Bian, Lei Jiang, NeurIPS2020
- Partially Encrypted Deep Learning using Functional Encryption, Théo Ryffel, David Pointcheval, Francis Bach, Edouard Dufour-Sans, Romain Gay, NeurIPS2019
- SHE: A Fast and Accurate Deep Neural Network for Encrypted Data, Qian Lou, Lei Jiang, NeurIPS2019
- 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
- Low Latency Privacy Preserving Inference, Alon Brutzkus, Ran Gilad-Bachrach, Oren Elisha, ICML2019
- TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service, Amartya Sanyal, Matt Kusner, Adria Gascon, Varun Kanade, ICML2018
- 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
- SAFENet: A Secure, Accurate and Fast Neural Network Inference, Qian Lou, Yilin Shen, Hongxia Jin, Lei Jiang, ICLR2021
- 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
- CRYPTOGRU: Low Latency Privacy-Preserving Text Analysis With GRU, Bo Feng, Qian Lou, Lei Jiang, Geoffrey Fox, EMNLP2021
- Privacy-Enhanced Machine Learning with Functional Encryption, Tilen Marc, Miha Stopar, Jan Hartman, Manca Bizjak, Jolanda Modic, ESORICS2019, code
- EzPC: Programmable and Efficient Secure Two-Party Computation for Machine Learning, Nishanth Chandran, Divya Gupta, Aseem Rastogi, Rahul Sharma, Shardul Tripathi, EuroSP2019, code