Methods for secure learning of parameters of a convolution neural network, and for secure input data classification
US11003991B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 2, 2017 |
| Grant date | May 11, 2021 |
| Priority date | — |
| Expiry date | Mar 12, 2040 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L9/008
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method for secure learning of parameters of a convolution neural network, CNN, for data classification includes the implementation, by data processing of a first server, including receiving from a second server a base of already classified learning data, the learning data being homomorphically encrypted; learning in the encrypted domain, from the learning database, the parameters of a reference CNN including a non-linear layer (POLYNOMIAL) operating an at least two-degree polynomial function approximating an activation function; a batch normalization layer before each non-linear layer (POLYNOMIAL); and transmitting the learnt parameters to the second server, for decryption and use for classification.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.