Encrypted Machine Learning
Encrypted Machine Learning is a computational framework that enables machine learning models to perform training and inference directly on encrypted data using homomorphic encryption, thereby preserving data privacy while maintaining the functionality of the learning algorithm.
183
Publications
11.2K
Citations
642
Authors
268
Institutions
Publications per year
2017–2026
158
Parents
183
Privacy-Preserving Ridge Regression on Hundreds of Millions of Records
Valeria Nikolaenko, Udi Weinsberg, Stratis Ioannidis et al. · 2013 · 451 citations · Full text
Secure Outsourced Matrix Computation and Application to Neural Networks
Xiaoqian Jiang, Miran Kim, Kristin Lauter et al. · 2018 · 319 citations
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268
Seoul National University
Seoul, South Korea
6 h-index
19 publications
1.1K citations
Microsoft (United States)
Redmond, United States
5 h-index
12 publications
7.5K citations
University of Southern California
Los Angeles, United States
4 h-index
9 publications
159 citations
University of California San Diego
San Diego, United States
4 h-index
5 publications
424 citations

Chinese Academy of Sciences
Beijing, China
4 h-index
12 publications
763 citations
National University of Singapore
Singapore, Singapore
4 h-index
6 publications
311 citations

Nanjing University of Aeronautics and Astronautics
Nanjing, China
4 h-index
12 publications
504 citations
Agency for Science, Technology and Research
Singapore, Singapore
4 h-index
10 publications
309 citations
1–8 of 268
Repository · 22 publications · 1.1K citations
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12 publications · 587 citations
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Repository · 8 publications · 201 citations
8
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4 publications · 214 citations
4
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3 publications · 359 citations
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