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Prior work on Private Inference (PI) -- inferences performed directly on encrypted input -- has focused on minimizing a network's ReLUs, which have been assumed to dominate PI latency rather than FLOPs.
How to share a secret
Adi Shamir · 1979
Earlier work this paper cites.
How to generate and exchange secrets
Andrew Chi-Chih Yao · 1986
Earlier work this paper cites.
A fully homomorphic encryption scheme
Craig Gentry et al · 2009
Earlier work this paper cites.
Cifar-10 (canadian institute for advanced research)
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton · 2010
Earlier work this paper cites.
Somewhat practical fully homomorphic encryption
Junfeng Fan and Frederik Vercauteren · 2012
Earlier work this paper cites.
(leveled) fully homomorphic encryption without bootstrapping
Zvika Brakerski, Craig Gentry, and Vinod Vaikuntanathan · 2014
Earlier work this paper cites.
How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
Earlier work this paper cites.
Aby-a framework for efficient mixed-protocol secure two-party computation
Daniel Demmler, Thomas Schneider, and Michael Zohner · 2015
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
Earlier work this paper cites.
Tiny imagenet visual recognition challenge
Ya Le and Xuan Yang · 2015
Earlier work this paper cites.
Tiny imagenet classification with convolutional neural networks
Leon Yao and John Miller · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
Earlier work this paper cites.
Garbling gadgets for boolean and arithmetic circuits
Marshall Ball, Tal Malkin, and Mike Rosulek · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel · 2016
Earlier work this paper cites.
Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
Earlier work this paper cites.
Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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Homomorphic encryption for arithmetic of approximate numbers
Jung Hee Cheon, Andrey Kim, Miran Kim, and Yongsoo Song · 2017
Earlier work this paper cites.
Steerable CNNs
Taco S. Cohen and Max Welling · 2017
Earlier work this paper cites.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
Earlier work this paper cites.
Oblivious neural network predictions via minionn transformations
Jian Liu, Mika Juuti, Yao Lu, and N Asokan · 2017
Earlier work this paper cites.
Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
Earlier work this paper cites.
Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification
Julie Chang, Vincent Sitzmann, Xiong Dun, Wolfgang Heidrich, and Gordon Wetzstein · 2018
Earlier work this paper cites.
Gazelle: A low latency framework for secure neural network inference
Chiraag Juvekar, Vinod Vaikuntanathan, and Anantha Chandrakasan · 2018
Earlier work this paper cites.
Darts: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2018
Earlier work this paper cites.
Aby3: A mixed protocol framework for machine learning
Payman Mohassel and Peter Rindal · 2018
Earlier work this paper cites.
Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
Earlier work this paper cites.
3d steerable cnns: Learning rotationally equivariant features in volumetric data
Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, and Taco S Cohen · 2018
Earlier work this paper cites.
Garbled neural networks are practical
Marshall Ball, Brent Carmer, Tal Malkin, Mike Rosulek, and Nichole Schimanski · 2019
Earlier work this paper cites.
Reconciling modern machine-learning practice and the classical bias–variance trade-off
Mikhail Belkin, Daniel Hsu, Siyuan Ma, and Soumik Mandal · 2019
Earlier work this paper cites.
Res2net: A new multi-scale backbone architecture
Shanghua Gao, Ming-Ming Cheng, Kai Zhao, Xin-Yu Zhang, Ming-Hsuan Yang, and Philip HS Torr · 2019
Earlier work this paper cites.
Searching for mobilenetv3
Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, et al · 2019
Cited alongside, same era.
Wide neural networks of any depth evolve as linear models under gradient descent
Jaehoon Lee, Lechao Xiao, Samuel Schoenholz, Yasaman Bahri, Roman Novak, Jascha Sohl-Dickstein, and Jeffrey Pennington · 2019
Cited alongside, same era.
On network design spaces for visual recognition
Ilija Radosavovic, Justin Johnson, Saining Xie, Wan-Yen Lo, and Piotr Dollár · 2019
Cited alongside, same era.
Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
Cited alongside, same era.
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
Cited alongside, same era.
General e(2)-equivariant steerable cnns
Iron: Private inference on transformers
Meng Hao, Hongwei Li, Hanxiao Chen, Pengzhi Xing, Guowen Xu, and Tianwei Zhang · 2022
Later among the works it cites.
Cheetah: Lean and fast secure Two-Party deep neural network inference
Zhicong Huang, Wen jie Lu, Cheng Hong, and Jiansheng Ding · 2022
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Scaling up trustless dnn inference with zero-knowledge proofs
Daniel Kang, Tatsunori Hashimoto, Ion Stoica, and Yi Sun · 2022
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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, and Woosuk Choi · 2022
Later among the works it cites.
All-optical ultrafast relu function for energy-efficient nanophotonic deep learning
Gordon HY Li, Ryoto Sekine, Rajveer Nehra, Robert M Gray, Luis Ledezma, Qiushi Guo, and Alireza Marandi · 2022
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Maurice Weiler and Gabriele Cesa · 2019
Cited alongside, same era.
Training for faster adversarial robustness verification via inducing reLU stability
Kai Y. Xiao, Vincent Tjeng, Nur Muhammad (Mahi) Shafiullah, and Aleksander Madry · 2019
Cited alongside, same era.
On polynomial approximations for privacy-preserving and verifiable relu networks
Ramy E Ali, Jinhyun So, and A Salman Avestimehr · 2020
Cited alongside, same era.
Adversarial training and provable defenses: Bridging the gap
Mislav Balunović and Martin Vechev · 2020
Cited alongside, same era.
CryptoNAS: Private inference on a relu budget
Zahra Ghodsi, Akshaj Kumar Veldanda, Brandon Reagen, and Siddharth Garg · 2020
Cited alongside, same era.
Learning filter pruning criteria for deep convolutional neural networks acceleration
Yang He, Yuhang Ding, Ping Liu, Linchao Zhu, Hanwang Zhang, and Yi Yang · 2020
Cited alongside, same era.
Delphi: A cryptographic inference service for neural networks
Pratyush Mishra, Ryan Lehmkuhl, Akshayaram Srinivasan, Wenting Zheng, and Raluca Ada Popa · 2020
Cited alongside, same era.
A convnet for the 2020s
Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie · 2022
Later among the works it cites.
Craterlake: a hardware accelerator for efficient unbounded computation on encrypted data
Nikola Samardzic, Axel Feldmann, Aleksandar Krastev, Nathan Manohar, Nicholas Genise, Srinivas Devadas, Karim Eldefrawy, Chris Peikert, and Daniel Sanchez · 2022
Later among the works it cites.
Microsoft SEAL (release 4.0)
SEAL · 2022
Later among the works it cites.
Can neural nets learn the same model twice? investigating reproducibility and double descent from the decision boundary perspective
Gowthami Somepalli, Liam Fowl, Arpit Bansal, Ping Yeh-Chiang, Yehuda Dar, Richard Baraniuk, Micah Goldblum, and Tom Goldstein · 2022
Later among the works it cites.
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
Later among the works it cites.
Decoupled knowledge distillation
Borui Zhao, Quan Cui, Renjie Song, Yiyu Qiu, and Jiajun Liang · 2022
Later among the works it cites.
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, et al · 2023
Closest in time.
Privformer: Privacy-preserving transformer with mpc
Yoshimasa Akimoto, Kazuto Fukuchi, Youhei Akimoto, and Jun Sakuma · 2023
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Characterizing and optimizing end-to-end systems for private inference
Karthik Garimella, Zahra Ghodsi, Nandan Kumar Jha, Siddharth Garg, and Brandon Reagen · 2023
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Sigma: Secure gpt inference with function secret sharing
Kanav Gupta, Neha Jawalkar, Ananta Mukherjee, Nishanth Chandran, Divya Gupta, Ashish Panwar, and Rahul Sharma · 2023
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Ciphergpt: Secure two-party gpt inference
Xiaoyang Hou, Jian Liu, Jingyu Li, Yuhan Li, Wen-jie Lu, Cheng Hong, and Kui Ren · 2023
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Sharp: A short-word hierarchical accelerator for robust and practical fully homomorphic encryption
Jongmin Kim, Sangpyo Kim, Jaewan Choi, Jaiyoung Park, Donghwan Kim, and Jung Ho Ahn · 2023
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Learning to linearize deep neural networks for secure and efficient private inference
Souvik Kundu, Shunlin Lu, Yuke Zhang, Jacqueline Liu, and Peter A Beerel · 2023
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MPCFORMER: FAST, PERFORMANT AND PRIVATE TRANSFORMER INFERENCE WITH MPC
Dacheng Li, Hongyi Wang, Rulin Shao, Han Guo, Eric Xing, and Hao Zhang · 2023
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Haac: A hardware-software co-design to accelerate garbled circuits
Jianqiao Mo, Jayanth Gopinath, and Brandon Reagen · 2023
Closest in time.
Sok: Cryptographic neural-network computation
Lucien KL Ng and Sherman SM Chow · 2023
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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, et al · 2023
Closest in time.
Rpu: The ring processing unit
Deepraj Soni, Negar Neda, Naifeng Zhang, Benedict Reynwar, Homer Gamil, Benjamin Heyman, Mohammed Nabeel, Ahmad Al Badawi, Yuriy Polyakov, Kellie Canida, et al · 2023
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zkdl: Efficient zero-knowledge proofs of deep learning training
Haochen Sun and Hongyang Zhang · 2023
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Convnext v2: Co-designing and scaling convnets with masked autoencoders
Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon, and Saining Xie · 2023
Closest in time.
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, and Peter A. Beerel · 2023
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Primer: Fast private transformer inference on encrypted data
Mengxin Zheng, Qian Lou, and Lei Jiang · 2023
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Heap: A fully homomorphic encryption accelerator with parallelized bootstrapping
Rashmi Agrawal, Anantha Chandrakasan, and Ajay Joshi · 2024
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Autofhe: Automated adaption of cnns for efficient evaluation over fhe
Wei Ao and Vishnu Naresh Boddeti · 2024
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Morphling: A throughput-maximized tfhe-based accelerator using transform-domain reuse
Adiwena Putra, Joo-Young Kim, et al · 2024
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Converting transformers to polynomial form for secure inference over homomorphic encryption
Itamar Zimerman, Moran Baruch, Nir Drucker, Gilad Ezov, Omri Soceanu, and Lior Wolf · 2024
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