Privacy enhancing deep learning cloud service using a trusted execution environment
US11443182B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 25, 2018 |
| Grant date | Sep 13, 2022 |
| Priority date | — |
| Expiry date | Jul 15, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F2221/034
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Mechanisms are provided to implement an enhanced privacy deep learning system framework (hereafter “framework”). The framework receives, from a client computing device, an encrypted first subnet model of a neural network, where the first subnet model is one partition of multiple partitions of the neural network. The framework loads the encrypted first subnet model into a trusted execution environment (TEE) of the framework, decrypts the first subnet model, within the TEE, and executes the first subnet model within the TEE. The framework receives encrypted input data from the client computing device, loads the encrypted input data into the TEE, decrypts the input data, and processes the input data in the TEE using the first subnet model executing within the TEE.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.