Protecting machine learning models from privacy attacks
US11755743B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 3, 2019 |
| Grant date | Sep 12, 2023 |
| Priority date | — |
| Expiry date | Dec 31, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N5/02
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
This disclosure describes methods and systems for protecting machine learning models against privacy attacks. A machine learning model may be trained using a set of training data and causal relationship data. The causal relationship data may describe a subset of features in the training data that have a causal relationship with the outcome. The machine learning model may learn a function that predicts an outcome based on the training data and the causal relationship data. A predefined privacy guarantee value may be received. An amount of noise may be added to the machine learning model to make a privacy guarantee value of the machine learning model equivalent to or stronger than the predefined privacy guarantee value. The amount of noise may be added at a parameter level of the machine learning model.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.