BACKGROUND

1. Field

The present disclosure relates generally to machine learning and, more specifically, to privacy and proprietary-information preserving collaborative multi-party machine learning.

2. Description of the Related Art

It is frequently the case that one entity stores information relevant to a machine-learning based determination being made by another entity, but is not able to share that information. For example, one party may be prohibited from sharing information due to security constraints, regulatory constraints, or contractual constraints. As a result, machine-learning based determinations made by the other entity may be impaired by the lack of access to potentially relevant information, and parties may design systems that are globally sub-optimal across a set of such entities.

SUMMARY

The following is a non-exhaustive listing of some aspects of the present techniques. These and other aspects are described in the following disclosure.

Some aspects include a process, including: obtaining, with one or more processors, a first trained machine learning model, wherein: the first machine learning model is trained on a first training set that includes data the first entity is not permitted to provide to a second entity, the first trained machine learning model is configured to output tokens, and the tokens do not reveal more than a threshold amount of information about the data the first entity is not permitted to provide to the second entity; receiving, with one or more processors, a first set of input features with the first trained machine learning model and, in response, outputting a first token; and causing, with one or more processors, the first token and a first value associated with the first token to be input into second trained machine learning model accessible to the second entity, wherein the first value associated with the first token is a token-context value corresponding to the first token.

Some aspects include a tangible, non-transitory, machine-readable medium storing instructions that when executed by a data processing apparatus cause the data processing apparatus to perform operations including the above-mentioned process.

Some aspects include a system, including: one or more processors; and memory storing instructions that when executed by the processors cause the processors to effectuate operations of the above-mentioned process.

