Method for protecting a machine learning ensemble from copying

A method is provided for protecting a machine learning ensemble. In the method, a plurality of machine learning models is combined to form a machine learning ensemble. A plurality of data elements for training the machine learning ensemble is provided. The machine learning ensemble is trained using...

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Bibliographic Details
Main Authors Derks, Gerardus Antonius Franciscus, Michiels, Wilhelmus Petrus Adrianus Johannus
Format Patent
LanguageEnglish
Published 11.10.2022
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
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Summary:A method is provided for protecting a machine learning ensemble. In the method, a plurality of machine learning models is combined to form a machine learning ensemble. A plurality of data elements for training the machine learning ensemble is provided. The machine learning ensemble is trained using the plurality of data elements to produce a trained machine learning ensemble. During an inference operating phase, an input is received by the machine learning ensemble. A piecewise function is used to pseudo-randomly choose one of the plurality of machine learning models to provide an output in response to the input. The use of a piecewise function hides which machine learning model provided the output, making the machine learning ensemble more difficult to copy.
Bibliography:Application Number: US201816145287