Trusted execution environment-based model training methods and apparatuses

A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations comprising:

• obtaining encrypted target samples from an encrypted training sample set in a first execution environment;
• inputting the encrypted target samples into a second execution environment, wherein the second execution environment is a trusted execution environment (TEE) different from the first execution environment;
• decrypting the encrypted target samples in the TEE to obtain decrypted target samples;
• inputting the decrypted target samples into a feature extraction model in the TEE to determine sample features;
• determining the sample features output from the TEE as target sample features for a current iteration of a training process for a target model;
• and
• performing, based on the target sample features, the current iteration on the target model in the first execution environment.

A computer-implemented system, comprising:

• one or more computers;
• and
• one or more computer memory devices interoperably coupled with the one or more computers and having tangible, non-transitory, machine-readable media storing one or more instructions that, when executed by the one or more computers, perform one or more operations comprising:
• obtaining encrypted target samples from an encrypted training sample set in a first execution environment;
• inputting the encrypted target samples into a second execution environment,
• wherein the second execution environment is a trusted execution environment (TEE) different from the first execution environment;
• decrypting the encrypted target samples in the TEE to obtain decrypted target samples;
• inputting the decrypted target samples into a feature extraction model in the TEE to determine sample features;
• determining the sample features output from the TEE as target sample features for a current iteration of a training process for a target model;
• and
• performing, based on the target sample features, the current iteration on the target model in the first execution environment.