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Designing privacy-preserving deep learning models is a major challenge within the deep learning community.
Sur la détermination des polynômes d’approximation de degré donnée
Eugene Y Remez. 1934 · 1934
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A fully homomorphic encryption scheme
Craig Gentry. 2009 · 2009
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Barycentric-remez algorithms for best polynomial approximation in the chebfun system
Ricardo Pachón and Lloyd N Trefethen. 2009 · 2009
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton. 2012 · 2012
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Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy
Ran Gilad Bachrach, Nathan Dowlin, Kim Laine, Kristin Lauter, Michael Naehrig, and John Wernsing. 2016 · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
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Homomorphic encryption for arithmetic of approximate numbers
Jung Hee Cheon, Andrey Kim, Miran Kim, and Yongsoo Song. 2017 · 2017
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Cryptodl: Deep neural networks over encrypted data
Ehsan Hesamifard, Hassan Takabi, and Mehdi Ghasemi. 2017 · 2017
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Secureml: A system for scalable privacy-preserving machine learning
Payman Mohassel and Yupeng Zhang. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Chet: An optimizing compiler for fully-homomorphic neural-network inferencing
Roshan Dathathri, Olli Saarikivi, Hao Chen, Kim Laine, Kristin Lauter, Saeed Maleki, Madanlal Musuvathi, and Todd Mytkowicz. 2019 · 2019
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Privacy preserving neural network inference on encrypted data with gpus
Daniel Takabi, Robert Podschwadt, Jeff Druce, Curt Wu, and Kevin Procopio. 2019 · 2019
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Polynomial activation neural networks: Modeling, stability analysis and coverage bp-training
Jun Zhou, Huimin Qian, Xinbiao Lu, Zhaoxia Duan, Haoqian Huang, and Zhen Shao. 2019 · 2019
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P-nets: Deep polynomial neural networks
Grigorios G Chrysos, Stylianos Moschoglou, Giorgos Bouritsas, Yannis Panagakis, Jiankang Deng, and Stefanos Zafeiriou. 2020 · 2020
Cited alongside, same era.
A New Remez-Type Algorithm for Best Polynomial Approximation
Nadaniela Egidi, Lorella Fatone, and Luciano Misici. 2020 · 2020
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Polynomial activation functions
Vikas Gottemukkula. 2020 · 2020
Cited alongside, same era.
Improved polynomial neural networks with normalised activations
Mohit Goyal, Rajan Goyal, and Brejesh Lall. 2020 · 2020
Cited alongside, same era.
Precise approximation of convolutional neural networks for homomorphically encrypted data
Junghyun Lee, Eunsang Lee, Joon-Woo Lee, Yongjune Kim, Young-Sik Kim, and Jong-Seon No. 2021 · 2021
Cited alongside, same era.
Primer: Searching for efficient transformers for language modeling
Polynomial approximation of inverse sqrt function for fhe
Samanvaya Panda. 2022 · 2022
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Understanding the failure of batch normalization for transformers in nlp
Jiaxi Wang, Ji Wu, and Lei Huang. 2022 · 2022
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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 · 2023
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Autofhe: Automated adaption of cnns for efficient evaluation over fhe
Wei Ao and Vishnu Naresh Boddeti. 2023 · 2023
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Sensitive tuning of large scale cnns for e2e secure prediction using homomorphic encryption
Moran Baruch, Nir Drucker, Gilad Ezov, Eyal Kushnir, Jenny Lerner, Omri Soceanu, and Itamar Zimerman. 2023 · 2023
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David R So, Wojciech Mańke, Hanxiao Liu, Zihang Dai, Noam Shazeer, and Quoc V Le. 2021 · 2021
Cited alongside, same era.
Leveraging batch normalization for vision transformers
Zhuliang Yao, Yue Cao, Yutong Lin, Ze Liu, Zheng Zhang, and Han Hu. 2021 · 2021
Cited alongside, same era.
A Methodology for Training Homomorphic Encryption Friendly Neural Networks
Moran Baruch, Nir Drucker, Lev Greenberg, and Guy Moshkowich. 2022 · 2022
Cited alongside, same era.
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 · 2022
Cited alongside, same era.
Iron: Private inference on transformers
Meng Hao, Hongwei Li, Hanxiao Chen, Pengzhi Xing, Guowen Xu, and Tianwei Zhang. 2022 · 2022
Cited alongside, same era.
Transformer quality in linear time
Weizhe Hua, Zihang Dai, Hanxiao Liu, and Quoc Le. 2022 · 2022
Cited alongside, same era.
A convnet for the 2020s
Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. 2022 · 2022
Cited alongside, same era.
East: Efficient and accurate secure transformer framework for inference
Yuanchao Ding, Hua Guo, Yewei Guan, Weixin Liu, Jiarong Huo, Zhenyu Guan, and Xiyong Zhang. 2023 · 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 · 2023
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Hetal: Efficient privacy-preserving transfer learning with homomorphic encryption
Seewoo Lee, Garam Lee, Jung Woo Kim, Junbum Shin, and Mun-Kyu Lee. 2023 · 2023
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Merge: Fast private text generation
Zi Liang, Pinghui Wang, Ruofei Zhang, Nuo Xu, and Shuo Zhang. 2023 · 2023
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Llms can understand encrypted prompt: Towards privacy-computing friendly transformers
Xuanqi Liu and Zhuotao Liu. 2023 · 2023
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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, and Ru Huang. 2023 · 2023
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Primer: Fast private transformer inference on encrypted data
Mengxin Zheng, Qian Lou, and Lei Jiang. 2023 · 2023
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