Privacy-preserving machine learning in the three-server model
US11222138B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 17, 2018 |
| Grant date | Jan 11, 2022 |
| Priority date | — |
| Expiry date | Jul 17, 2038 |
Classification
- Technology area (CPC A)Human Necessities
- CPC primaryA63B2244/10
- WIPO fieldFurniture, games
- WIPO sectorOther fields
Abstract
Methods and systems according to embodiments of the invention provide for a framework for privacy-preserving machine learning which can be used to obtain solutions for training linear regression, logistic regression and neural network models. Embodiments of the invention are in a three-server model, wherein data owners secret-share their data among three servers who train and evaluate models on the joint data using three-party computation (3PC). Embodiments of the invention provide for efficient conversions between arithmetic, binary, and Yao 3PC, as well as techniques for fixed-point multiplication and truncation of shared decimal values. Embodiments also provide customized protocols for evaluating polynomial piecewise functions and a three-party oblivious transfer protocol.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.