Abstract: The present specification discloses a trusted execution environment (TEE)-based model training method and apparatus. In one or more embodiments, the method includes: 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 that 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.

Abstract: A data processing method and a corresponding system are provided. The method is implemented by a processor and includes: obtaining a to-be-processed I/O request, where the to-be-processed I/O request may include a first address, and the first address is a logical address of to-be-read, to-be-written, or to-be-erased data in a target SSD; performing address translation on the to-be-processed I/O request based on an FTL mapping table, to translate the first address into a second address, where the second address is used to indicate a physical address of the to-be-read, to-be-written, or to-be-erased data in the target SSD, and the FTL mapping table may be used to record a translation relationship between physical addresses and logical addresses in the n SSDs; sending a to-be-processed I/O request obtained after address translation is performed; and after a sleep duration is preset, querying a processing result of the to-be-processed I/O request.

Abstract: The present specification discloses a trusted execution environment (TEE)-based model training method and apparatus. In one or more embodiments, the method includes: 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 that 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.

Abstract: A data processing method and a corresponding system are provided. The method is implemented by a processor and includes: obtaining a to-be-processed I/O request, where the to-be-processed I/O request may include a first address, and the first address is a logical address of to-be-read, to-be-written, or to-be-erased data in a target SSD; performing address translation on the to-be-processed I/O request based on an FTL mapping table, to translate the first address into a second address, where the second address is used to indicate a physical address of the to-be-read, to-be-written, or to-be-erased data in the target SSD, and the FTL mapping table may be used to record a translation relationship between physical addresses and logical addresses in the n SSDs; sending a to-be-processed I/O request obtained after address translation is performed; and after a sleep duration is preset, querying a processing result of the to-be-processed I/O request.