Private artificial neural networks with trusted execution environments and quadratic homomorphic encryption
US12388617B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 19, 2021 |
| Grant date | Aug 12, 2025 |
| Priority date | — |
| Expiry date | Jun 19, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F2207/4824
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method training an artificial neural network (ANN) on a remote host computes, using a trusted process deployed in a trusted execution environment (TEE) on the remote host, a key-pair for a homomorphic encryption scheme and shares, by the trusted process, the public key (PK) of the key-pair with an untrusted process deployed on the remote host. The method splits the training procedure of the ANN between the untrusted process and the trusted process, wherein the untrusted process computes encrypted inputs to neurons of the ANN by means of the homomorphic encryption scheme, while the trusted process computes outputs of the neurons based on the respective encrypted inputs to the neurons as provided by the untrusted process.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.