System for measuring information leakage of deep learning models
US11886989B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 10, 2018 |
| Grant date | Jan 30, 2024 |
| Priority date | — |
| Expiry date | Nov 25, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/048
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Using a deep learning inference system, respective similarities are measured for each of a set of intermediate representations to input information used as an input to the deep learning inference system. The deep learning inference system includes multiple layers, each layer producing one or more associated intermediate representations. Selection is made of a subset of the set of intermediate representations that are most similar to the input information. Using the selected subset of intermediate representations, a partitioning point is determined in the multiple layers used to partition the multiple layers into two partitions defined so that information leakage for the two partitions will meet a privacy parameter when a first of the two partitions is prevented from leaking information. The partitioning point is output for use in partitioning the multiple layers of the deep learning inference system into the two partitions.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.