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MPCache: MPC-Friendly KV Cache Eviction for Efficient Private LLM Inference , Wenxuan Zeng, Wenxuan_Zeng, Ye Dong, Jinjin Zhou, Junming Ma, Jin Tan, Runsheng Wang, Meng Li, NeurIPS2025, code 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, ICCV2023Learning to Linearize Deep Neural Networks for Secure and Efficient Private Inference , Souvik Kundu, Shunlin Lu, Yuke Zhang, Jacqueline Tiffany Liu, Peter A. Beerel, ICLR2023LinGCN: 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, ICCV2023Selective 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
Powerformer: Efficient and High-Accuracy Privacy-Preserving Language Model with Homomorphic Encryption , Dongjin Park, Eunsang Lee, Joon-Woo Lee, ACL2025, code Secure Transformer Inference Made Non-interactive , Jiawen Zhang, Xinpeng Yang, Lipeng He, Kejia Chen, Wen-jie Lu, Yinghao Wang, Xiaoyang Hou, Jian Liu, Kui Ren, Xiaohu Yang, NDSS2025, code SHAFT: Secure, Handy, Accurate, and Fast Transformer Inference , Andes Y. L. Kei, Sherman S. M. Chow, NDSS2025, code EncryptedLLM: Privacy-Preserving Large Language Model Inference via GPU-Accelerated Fully Homomorphic Encryption , Leo de Castro, Daniel Escudero, Daniel_Escudero, Adya Agrawal, Antigoni Polychroniadou, Manuela Veloso, ICML2025CryptGNN: Enabling Secure Inference for Graph Neural Networks , Pritam Sen, Yao Ma, Cristian Borcea, CCS2025WPC: Weight Plaintext Compression for CNN Inference based on RNS-CKKS , CCS2025Mosformer: Maliciously Secure Three-Party Inference Framework
for Large Transformers , Ke Cheng, Yuheng Xia, Anxiao Song, Jiaxuan Fu, Wenjie Qu, Yulong Shen, Jiaheng Zhang, CCS2025, code THOR: Secure Transformer Inference with Homomorphic Encryption , Jungho Moon, Dongwoo Yoo, Xiaoqian Jiang, Miran Kim, CCS2025CipherPrune: Efficient and Scalable Private Transformer Inference ,Yancheng Zhang, Yancheng Zhang, Jiaqi Xue, Mengxin Zheng, Mimi Xie, Mingzhe Zhang, Lei Jiang, Qian Lou, ICLR2025, code SelectFormer in Data Markets: Privacy-Preserving and Efficient Data Selection for Transformers with Multi-Party Computation , Xu Ouyang, Felix Xiaozhu Lin, Yangfeng Ji, ICLR2025DCT-CryptoNets: Scaling Private Inference in the Frequency Domain , Arjun Roy, Kaushik Roy, ICLR2025, code Encryption-Friendly LLM Architecture , Donghwan Rho, Taeseong Kim, Minje Park, Jung Woo Kim, Hyunsik Chae, Ernest K. Ryu, Jung Hee Cheon, ICLR2025, code LOHEN: Layer-wise Optimizations for Neural Network Inferences over Encrypted Data with High Performance or Accuracy , Kevin Nam, Youyeon Joo, Dongju Lee, Seungjin Ha, Hyunyoung Oh, Gachon University; Hyungon Moon, Yunheung Paek, USENIX2025, code Breaking the Layer Barrier: Remodeling Private Transformer Inference with Hybrid CKKS and MPC , Tianshi Xu, Wen-jie Lu, Jiangrui Yu, Yi Chen, Chenqi Lin, Runsheng Wang, Meng Li, USENIX2025, code SLOTHE: Lazy Approximation of Non-Arithmetic Neural Network Functions over Encrypted Data , Kevin Nam, Youyeon Joo, Seungjin Ha, Yunheung Paek, USENIX2025, code CryptoMoE: Privacy-Preserving and Scalable Mixture of Experts Inference via Balanced Expert Routing , Yifan Zhou, Tianshi Xu, Jue Hong, Ye Wu, Meng Li, NeurIPS2025, code CryptoFace: End-to-End Encrypted Face Recognition , Wei Ao, VishnuNaresh Boddeti, CVPR2025, code
Orca: FSS-based Secure Training and Inference with GPUs , Neha Jawalkar, Kanav Gupta, Arkaprava Basu, Nishanth Chandran, Divya Gupta, Rahul Sharma, SP2024BOLT: Privacy-Preserving, Accurate and Efficient Inference for Transformers , Qi Pang, Jinhao Zhu, Helen Möllering, Wenting Zheng, Thomas Schneider, SP2024, code Pencil: Private and Extensible Collaborative Learning without the Non-Colluding Assumption , Xuanqi Liu, Zhuotao Liu, Qi Li, Ke Xu, Mingwei Xu, NDSS2024, 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, ICML2024Ditto: 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, CCS2024Rhombus: Fast Homomorphic Matrix-Vector Multiplication for Secure Two-Party Inference , Jiaxing He, Kang Yang, Guofeng Tang, Zhangjie Huang, Li Lin, Changzheng Wei, Ying Yan, Wei Wang, CCS2024Nimbus: Secure and Efficient Two-Party Inference for Transformers , Zhengyi Li, Kang Yang, Jin Tan, Wen-jie Lu, Haoqi Wu, Xiao Wang, Yu Yu, Derun Zhao, Yancheng Zheng, Minyi Guo, Jingwen Leng, NeurIPS2024, code SecFormer: Fast and Accurate Privacy-Preserving Inference for Transformer Models via SMPC , Jinglong Luo, Yehong Zhang, Zhuo Zhang, Jiaqi Zhang, Xin Mu, Hui Wang, Yue Yu, Zenglin Xu, ACL Findings2024, code PIPO: Privacy-Preserving Convolutional Neural Network Inference with Plaintext Operations , Tian Zhou, Lixin Gao, ICDCS2024, code
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, NeurIPS2023CoPriv: Network/Protocol Co-Optimization for Communication-Efficient Private Inference , Wenxuan Zeng, Meng Li, Haichuan Yang, Wen-jie Lu, Runsheng Wang, Ru Huang, NeurIPS2023Convolutions in Overdrive: Maliciously Secure Convolutions for MPC , Marc Rivinius, Pascal Reisert, Sebastian Hasler, and Ralf Küsters, PETS2023HeLayers: 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, PETS2023HELiKs: 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, SP2023ShadowNet: 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, NDSS2023REDsec: Running Encrypted Discretized Neural Networks in Seconds , Lars Folkerts, Charles Gouert, Nektarios Georgios Tsoutsos, NDSS2023Secure Floating-Point Training , Deevashwer Rathee, Anwesh Bhattacharya, Divya Gupta, Rahul Sharma, Dawn Song, USENIX2023Bicoptor: 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, SP2023Primer: Fast Private Transformer Inference on Encrypted Data , Mengxin Zheng, Qian Lou, Lei Jiang, DAC2023
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, SP2022SecFloat: 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, PETS2022Private and Reliable Neural Network Inference , Nikola Jovanovic, Marc Fischer, Samuel Steffen, Martin T. Vechev, CCS2022Tetrad: Actively Secure 4PC for Secure Training and Inference , Nishat Koti, Arpita Patra, Rahul Rachuri, Ajith Suresh, NDSS2022Piranha: A GPU Platform for Secure Computation , Jean-Luc Watson, Sameer Wagh, Raluca Ada Popa, USENIX2022Cheetah: Lean and Fast Secure Two-Party Deep Neural Network Inference , Zhicong Huang, Wen-jie Lu, Cheng Hong, Jiansheng Ding, USENIX2022, code
SAFENet: A Secure, Accurate and Fast Neural Network Inference , Qian Lou, Yilin Shen, Hongxia Jin, Lei Jiang, ICLR2021CRYPTOGRU: Low Latency Privacy-Preserving Text Analysis With GRU , Bo Feng, Qian Lou, Lei Jiang, Geoffrey Fox, EMNLP2021CrypTen: 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, NeurIPS2021Falcon: 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 2021SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning , Nishat Koti, Mahak Pancholi, Arpita Patra, Ajith Suresh, USENIX2021Muse: 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, USENIX2021Fantastic Four: Honest-Majority Four-Party Secure Computation With Malicious Security , Anders P. K. Dalskov, Daniel Escudero, Marcel Keller, USENIX2021CryptGPU: 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
AutoPrivacy: Automated Layer-wise Parameter Selection for Secure Neural Network Inference , Qian Lou, Song Bian, Lei Jiang, NeurIPS2020Falcon: Fast Spectral Inference on Encrypted Data , Qian Lou, Wen-jie Lu, Cheng Hong, Lei Jiang, NeurIPS2020Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data , Qian Lou, Bo Feng, Geoffrey Charles Fox, Lei Jiang, NeurIPS2020BLAZE: Blazing Fast Privacy-Preserving Machine Learning , Arpita Patra, Ajith Suresh, NDSS2020Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning , Harsh Chaudhari, Rahul Rachuri, Ajith Suresh, NDSS2020Delphi: 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, CVPR2020FALCON: 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
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, ICML2019SHE: 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, NeurIPS2019QUOTIENT: Two-Party Secure Neural Network Training and Prediction , Nitin Agrawal, Ali Shahin Shamsabadi, Matt J. Kusner, Adrià Gascón, CCS2019SecureNN: 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, CCS2019XONN: XNOR-based Oblivious Deep Neural Network Inference , M. Sadegh Riazi, Mohammad Samragh, Hao Chen, Kim Laine, Kristin E. Lauter, Farinaz Koushanfar, USENIX2019Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware , Florian Tramèr, Dan Boneh, ICLR2019, code
TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service , Amartya Sanyal, Matt Kusner, Adria Gascon, Varun Kanade, ICML2018ABY3: 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
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