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Secure multi-party computation (MPC) enables computation directly on encrypted data and protects both data and model privacy in deep learning inference.
How to share a secret
Adi Shamir · 1979
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Andrew C Yao · 1982
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Secure multi-party computation
Oded Goldreich · 1998
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Computationally secure oblivious transfer
Moni Naor and Benny Pinkas · 2005
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More efficient oblivious transfer and extensions for faster secure computation
Gilad Asharov, Yehuda Lindell, Thomas Schneider, and Michael Zohner · 2013
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Newton raphson method
Saba Akram and Quarrat Ul Ann · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, Jeff Dean, et al · 2015
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Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel · 2016
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From softmax to sparsemax: A sparse model of attention and multi-label classification
Andre Martins and Ramon Astudillo · 2016
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Secureml: A system for scalable privacy-preserving machine learning
Payman Mohassel and Yupeng Zhang · 2017
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Aby3: A mixed protocol framework for machine learning
Payman Mohassel and Peter Rindal · 2018
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Non-local neural networks
Xiaolong Wang, Ross Girshick, Abhinav Gupta, and Kaiming He · 2018
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Chamnet: Towards efficient network design through platform-aware model adaptation
Xiaoliang Dai, Peizhao Zhang, Bichen Wu, Hongxu Yin, Fei Sun, Yanghan Wang, Marat Dukhan, Yunqing Hu, Yiming Wu, Yangqing Jia, et al · 2019
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Irlas: Inverse reinforcement learning for architecture search
Minghao Guo, Zhao Zhong, Wei Wu, Dahua Lin, and Junjie Yan · 2019
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Reinforcement learning for neural architecture search: A review
Yesmina Jaafra, Jean Luc Laurent, Aline Deruyver, and Mohamed Saber Naceur · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova · 2019
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Are sixteen heads really better than one?
Paul Michel, Omer Levy, and Graham Neubig · 2019
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Mnasnet: Platform-aware neural architecture search for mobile
Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard, and Quoc V Le · 2019
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Analyzing multi-head self-attention: Specialized heads do the heavy lifting, the rest can be pruned
Elena Voita, David Talbot, Fedor Moiseev, Rico Sennrich, and Ivan Titov · 2019
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Haq: Hardware-aware automated quantization with mixed precision
Kuan Wang, Zhijian Liu, Yujun Lin, Ji Lin, and Song Han · 2019
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Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search
Bichen Wu, Xiaoliang Dai, Peizhao Zhang, Yanghan Wang, Fei Sun, Yiming Wu, Yuandong Tian, Peter Vajda, Yangqing Jia, and Kurt Keutzer · 2019
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Cryptonas: Private inference on a relu budget
Zahra Ghodsi, Akshaj Kumar Veldanda, Brandon Reagen, and Siddharth Garg · 2020
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Hardware/software co-exploration of neural architectures
Weiwen Jiang, Lei Yang, Edwin Hsing-Mean Sha, Qingfeng Zhuge, Shouzhen Gu, Sakyasingha Dasgupta, Yiyu Shi, and Jingtong Hu · 2020
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Reformer: The efficient transformer
Nikita Kitaev, Lukasz Kaiser, and Anselm Levskaya · 2020
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Safenet: A secure, accurate and fast neural network inference
Qian Lou, Yilin Shen, Hongxia Jin, and Lei Jiang · 2020
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Delphi: A cryptographic inference service for neural networks
Pratyush Mishra, Ryan Lehmkuhl, Akshayaram Srinivasan, Wenting Zheng, and Raluca Ada Popa · 2020
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Cryptflow2: Practical 2-party secure inference
Deevashwer Rathee, Mayank Rathee, Nishant Kumar, Nishanth Chandran, Divya Gupta, Aseem Rastogi, and Rahul Sharma · 2020
THE-X: Privacy-preserving transformer inference with homomorphic encryption
Tianyu Chen, Hangbo Bao, Shaohan Huang, Li Dong, Binxing Jiao, Daxin Jiang, Haoyi Zhou, Jianxin Li, and Furu Wei · 2022
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Selective network linearization for efficient private inference
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Sinong Wang, Belinda Z Li, Madian Khabsa, Han Fang, and Hao Ma · 2020
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Vivit: A video vision transformer
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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Escaping the big data paradigm with compact transformers
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Cheetah: Lean and fast secure Two-Party deep neural network inference
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Characterization of mpc-based private inference for transformer-based models
Yongqin Wang, G Edward Suh, Wenjie Xiong, Benjamin Lefaudeux, Brian Knott, Murali Annavaram, and Hsien-Hsin S Lee · 2022
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Structured pruning learns compact and accurate models
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Deepreshape: Redesigning neural networks for efficient private inference
Nandan Kumar Jha and Brandon Reagen · 2023
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Learning to linearize deep neural networks for secure and efficient private inference
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MPCFORMER: fast, performant and provate transformer inference with MPC
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SecretFlow-SPU: A performant and User-Friendly framework for Privacy-Preserving machine learning
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Rrnet: Towards relu-reduced neural network for two-party computation based private inference
Hongwu Peng, Shanglin Zhou, Yukui Luo, Nuo Xu, Shijin Duan, Ran Ran, Jiahui Zhao, Shaoyi Huang, Xi Xie, Chenghong Wang, et al · 2023
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