Falcon: Fast Spectral Inference on Encrypted Data

作者: Lei Jiang , Qian Lou , Wen-jie Lu , Cheng Hong

DOI:

关键词:

摘要:

参考文章(15)
Dan Boneh, Craig Gentry, A fully homomorphic encryption scheme Stanford University. ,(2009)
Martin R. Albrecht, Rachel Player, Sam Scott, On the concrete hardness of Learning with Errors Journal of Mathematical Cryptology. ,vol. 9, pp. 169- 203 ,(2015) , 10.1515/JMC-2015-0016
Ran Gilad-Bachrach, Kristin Lauter, John Wernsing, Michael Naehrig, Kim Laine, Nathan Dowlin, CryptoNets: applying neural networks to encrypted data with high throughput and accuracy international conference on machine learning. pp. 201- 210 ,(2016)
Xiaoqian Jiang, Miran Kim, Kristin Lauter, Yongsoo Song, Secure Outsourced Matrix Computation and Application to Neural Networks computer and communications security. ,vol. 2018, pp. 1209- 1222 ,(2018) , 10.1145/3243734.3243837
Fabian Boemer, Yixing Lao, Rosario Cammarota, Casimir Wierzynski, nGraph-HE: a graph compiler for deep learning on homomorphically encrypted data computing frontiers. pp. 3- 13 ,(2019) , 10.1145/3310273.3323047
Li Fei-Fei, Daniel Levy, Serena Yeung, Albert Haque, Edward Chou, Josh Beal, Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inference. arXiv: Cryptography and Security. ,(2018)
Ran Gilad-Bachrach, Alon Brutzkus, Oren Elisha, Low Latency Privacy Preserving Inference international conference on machine learning. pp. 812- 821 ,(2018)
Kyoohyung Han, Minki Hhan, Jung Hee Cheon, Improved Homomorphic Discrete Fourier Transforms and FHE Bootstrapping IEEE Access. ,vol. 7, pp. 57361- 57370 ,(2019) , 10.1109/ACCESS.2019.2913850
Oren Rippel, Jasper Snoek, Ryan P. Adams, Spectral Representations for Convolutional Neural Networks arXiv: Machine Learning. ,(2015)
Roshan Dathathri, Olli Saarikivi, Hao Chen, Kim Laine, Kristin Lauter, Saeed Maleki, Madanlal Musuvathi, Todd Mytkowicz, CHET: an optimizing compiler for fully-homomorphic neural-network inferencing programming language design and implementation. pp. 142- 156 ,(2019) , 10.1145/3314221.3314628