Method, device and computer program product for generating neural network model

An electronic device, comprising:

• at least one processor;
• and
• memory coupled to the at least one processor and having instructions stored therein, wherein the instructions, when executed by the at least one processor, cause the electronic device to execute actions comprising:
• deploying a processor-based trusted execution environment in a multi-party computation system;
• verifying, in the trusted execution environment of the multi-party computation system, a first entity that provides training data to be used in training a neural network model and a second entity that provides the neural network model to be trained;
• dividing, in the multi-party computation system, the neural network model into multiple parts based on a computational graph of the neural network model, wherein the multiple parts comprise a first part for processing an input to the neural network model, the input comprising at least a portion of the training data provided by the first entity, and a second part for receiving an output from the first part, wherein at least portions of respective ones of the first and second parts collectively comprise a sequence of serially-connected operators of the neural network model;
• converting, in the multi-party computation system and based on syntax for the trusted execution environment, a first part of code in source code of the neural network model and corresponding to the first part of the neural network model;
• compiling, in the multi-party computation system, the converted first part of code and a second part of code in the source code and corresponding to the second part of the neural network model, wherein compiling the converted first part of code and the second part of code further comprises (i) adding one or more enclave calls and (ii) adding one or more outside calls, to support communication between the trusted execution environment and an untrusted execution environment;
• and
• executing the compiled first part of code and the compiled second part of code respectively in the trusted execution environment and the untrusted execution environment to train the neural network model utilizing the input comprising at least a portion of the training data provided by the first entity.

A computer program product comprising a non-transitory computer-readable medium having machine-executable instructions stored therein, wherein the machine-executable instructions, when executed by a machine, cause the machine to perform actions comprising:

• deploying a processor-based trusted execution environment in a multi-party computation system;
• verifying, in the trusted execution environment of the multi-party computation system, a first entity that provides training data to be used in training a neural network model and a second entity that provides the neural network model to be trained;
• dividing, in the multi-party computation system, the neural network model into multiple parts based on a computational graph of the neural network model, wherein the multiple parts comprise a first part for processing an input to the neural network model, the input comprising at least a portion of the training data provided by the first entity, and a second part for receiving an output from the first part, wherein at least portions of respective ones of the first and second parts collectively comprise a sequence of serially-connected operators of the neural network model;
• converting, in the multi-party computation system and based on syntax for the trusted execution environment, a first part of code in source code of the neural network model and corresponding to the first part of the neural network model;
• compiling, in the multi-party computation system, the converted first part of code and a second part of code in the source code and corresponding to the second part of the neural network model, wherein compiling the converted first part of code and the second part of code further comprises (i) adding one or more enclave calls and (ii) adding one or more outside calls, to support communication between the trusted execution environment and an untrusted execution environment;
• and
• executing the compiled first part of code and the compiled second part of code respectively in the trusted execution environment and the untrusted execution environment to train the neural network model utilizing the input comprising at least a portion of the training data provided by the first entity.