Multi-party prediction using feature contribution values
US11848915B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 30, 2020 |
| Grant date | Dec 19, 2023 |
| Priority date | — |
| Expiry date | Jan 2, 2042 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L9/008
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques are provided for multi-party prediction using feature contribution values. One method comprises obtaining a first set of feature contribution values associated with respective ones of a plurality of machine learning models, wherein each machine learning model is trained using training data of a different party and each feature contribution value indicates a contribution by a corresponding feature to a prediction generated by the associated machine learning model; training an aggregate machine learning model using the obtained first sets of feature contribution values; receiving a second set of feature contribution values generated by applying data of at least one party to at least one machine learning model; and applying the second set of feature contribution values to the trained aggregate machine learning model to obtain a global prediction. Each feature contribution value may correspond to a masked feature, and the feature contribution values may not expose the source data of one party to another party.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.