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IEEE P2830 IEEE Approved Draft Standard for Technical Framework and Requirements of Trusted Execution Environment based Shared Machine Learning
standard by IEEE, 09/16/2021
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.
M00002083
2021-07-19
New product
IEEE P2830 IEEE Approved Draft Standard for Technical Framework and Requirements of Trusted Execution Environment based Shared Machine Learning
standard by IEEE, 09/16/2021