Abstract: The present disclosure provides a model training method; and the model training method includes: by a host process, acquiring training data, and dividing the training data for a training node cluster employing a master-workers architecture to obtain multiple pieces of sub-training data; wherein the training node cluster comprises a master node and multiple worker nodes; wherein the host process runs in a non-trusted execution environment, and the training node cluster runs in a trusted execution environment; by the host process, encrypting each piece of sub-training data, and storing the encrypted sub-training data in a shared memory of the host process; and controlling the master node and each of the worker nodes to acquire corresponding encrypted sub-training data from the shared memory in accordance with corresponding data storage addresses, respectively, and train a preset model by using corresponding respective decrypted sub-training data, respectively, to obtain a trained model.

Abstract: The present disclosure provides a data processing method and apparatus, a computer device and a computer storage medium, wherein the method includes: performing blinding processing on target information to be transmitted to obtain blinded information; acquiring a plurality of pieces of first encryption information obtained by respectively performing encryption processing on the blinded information by a plurality of service participation ends; performing data fusion on the plurality of pieces of first encryption information to obtain second encryption information; and performing de-blinding processing on the second encryption information to obtain third encryption information as a ciphertext identification corresponding to the target information.