"Enhancing Cloud Security through Efficient Polynomial Approximations for ..."

Bernardo Pulido-Gaytan et al. (2024)

> Home

Details and statistics

DOI: 10.1109/CCGRIDW63211.2024.00011

access: closed

type: Conference or Workshop Paper

metadata version: 2024-10-22

- view
  - electronic edition via DOI
  - unpaywalled version
  authority control:
- export record
  dblp key:
  - conf/ccgrid/Pulido-GaytanTB24
- ask others
- share record
  persistent URL:
  - https://dblp.org/rec/conf/ccgrid/Pulido-GaytanTB24
Bernardo Pulido-Gaytan, Andrei Tchemykh, Mikhail G. Babenko, Jorge M. Cortés-Mendoza, Horacio González-Vélez, Arutyun Avetisyan:
Enhancing Cloud Security through Efficient Polynomial Approximations for Homomorphic Evaluation of Neural Network Activation Functions. CCGridW 2024: 42-49

manage site settings

To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.