Abstract: Embodiments of this specification provide methods and apparatuses for performing model ownership verification based on an exogenous feature. An implementation of the methods includes: selecting initial samples from an initial sample set to form a selected sample set, processing sample data of the initial samples to obtain transform samples that form a transform sample set, training a meta-classifier based on a target model, an auxiliary model, and the transform sample set, inputting data associated with a suspicious model into the meta-classifier, and determining, based on an output result of the meta-classifier, whether the suspicious model is stolen from a deployment model, wherein the deployment model has feature knowledge of the exogenous feature.

Abstract: A machine learning model training method performed by a computer device acting as an active-party device is provided. The method includes: coding and encrypting a first object feature in the sample pairs provided by the active-party device using an active-party coding model to obtain an active-party first encrypted coding result; acquiring N passive-party first encrypted coding results correspondingly sent by N passive-party devices in combination with a second object feature in the sample pairs; splicing the active-party first encrypted coding result and the N passive-party first encrypted coding results to obtain a first spliced encrypted coding result, and applying the first spliced encrypted coding result to a first prediction model to obtain a first prediction probability; and causing an update of parameters of the different models based on a first difference between the first prediction probability and a first prediction task label of the sample pairs.