Multi-cloud privacy-preserving logistic regression
Clouds can significantly reduce the cost and time of business solutions. However, cloud services introduce significant security and privacy challenges when they process sensitive information. For instance, a dataset for machine learning could contain delicate information that traditional encryption...
Shranjeno v:
Главные авторы: | Babenko, M. G., Бабенко, М. Г. |
---|---|
Format: | Статья |
Jezik: | English |
Izdano: |
Springer Science and Business Media Deutschland GmbH
2022
|
Teme: | |
Online dostop: | https://dspace.ncfu.ru/handle/20.500.12258/18617 |
Oznake: |
Označite
Brez oznak, prvi označite!
|
Podobne knjige/članki
-
LR-GD-RNS: Enhanced privacy-preserving logistic regression algorithms for secure deployment in untrusted environments
od: Babenko, M. G., и др.
Izdano: (2021) -
Privacy-preserving logistic regression as a cloud service based on residue number system
od: Babenko, M. G., и др.
Izdano: (2021) -
Privacy-preserving neural networks with Homomorphic encryption: Challenges and opportunities
od: Babenko, M. G., и др.
Izdano: (2021) -
A survey on privacy-preserving machine learning with fully homomorphic encryption
od: Babenko, M. G., и др.
Izdano: (2021) -
A survey on multi-cloud storage security: threats and countermeasures
od: Bezuglova, E. S., и др.
Izdano: (2023)