Privacy-preserving statistical and machine learning methods under fully homomorphic encryption
<p>Advances in technology have now made it possible to monitor heart rate, body temperature and sleep patterns; continuously track movement; record brain activity; and sequence DNA in the jungle --- all using devices that fit in the palm of a hand. These and other recent developments have spar...
Κύριος συγγραφέας: | Esperança, P |
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Άλλοι συγγραφείς: | Holmes, C |
Μορφή: | Thesis |
Γλώσσα: | English |
Έκδοση: |
2016
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Παρόμοια τεκμήρια
Παρόμοια τεκμήρια
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Recent advances of privacy-preserving machine learning based on (Fully) Homomorphic Encryption
ανά: Hong Cheng
Έκδοση: (2025-01-01) -
Privacy-Preserving Machine Learning With Fully Homomorphic Encryption for Deep Neural Network
ανά: Joon-Woo Lee, κ.ά.
Έκδοση: (2022-01-01) -
Privacy-Preserving Feature Selection with Fully Homomorphic Encryption
ανά: Shinji Ono, κ.ά.
Έκδοση: (2022-06-01) -
A Survey of Deep Learning Architectures for Privacy-Preserving Machine Learning With Fully Homomorphic Encryption
ανά: Robert Podschwadt, κ.ά.
Έκδοση: (2022-01-01) -
Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning
ανά: Haokun Fang, κ.ά.
Έκδοση: (2021-04-01)