Full Description

Scope

This standard defines a framework and architectures for machine learning in which a model is trained using encrypted data that has been aggregated from multiple sources and is processed by a third party trusted execution environment. A distinctive feature of this technique is the essential use of a third party trusted execution environment for computations. The standard specifies functional components, workflows, security requirements, technical requirements, and protocols.

Purpose

There are many use cases in industries ranging from finance to healthcare to education where practitioners wish to apply machine learning to data sets that are aggregated from sources that cannot or should not be combined due to regulatory, competitive, or ethical considerations. Two fundamentally different approaches exist for addressing this. In "federated machine learning," models are trained by each source and the sources share the models but not the data themselves. In "shared machine learning" the data are shared but are encrypted and given to a trusted third party to train a model that is then shared. This standard will provide a verifiable basis for trust and security.

Abstract

New IEEE Standard - Active - Draft.

Product Details

Published: 09/16/2021 ISBN(s): 9781504476317 Number of Pages: 21 File Size: 1 file , 1.5 MB Product Code(s): STDAPE24747