Training data protection in artificial intelligence model execution environment

A system, comprising:

• a trusted execution area;
• and
• an untrusted execution area operatively coupled to the trusted execution area,
• wherein the trusted execution area is configured to execute a first portion of an artificial intelligence model and the untrusted execution area is configured to execute a second portion of the artificial intelligence model, wherein data at least one of obtained and processed in the first portion of the artificial intelligence model is inaccessible to the second portion of the artificial intelligence model;
• wherein a plurality of operations, customizable in accordance with one or more interfaces of a deep learning framework and executed by the system, each comprise one part executable in the trusted execution area and another part executable in the untrusted execution area, and wherein the one part and the other part of each of the plurality of operations communicate via one or more calls between the trusted execution area and the untrusted execution area;
• wherein the plurality of operations is computed with a feedforward pass prior to calculating gradients and updating parameters of the plurality of operations based on the computed plurality of operations in a backpropagation pass;
• and
• wherein the plurality of operations comprise:
• a first operation configured to assign the at least one of obtained and processed data into a first layer of the trusted execution area;
• a second operation configured to compute a first parameter associated with the feedforward pass, to update the first parameter based on the calculated gradients during the backpropagation pass, and to export the first parameter from the first portion of the artificial intelligence model to the second portion of the artificial intelligence model;
• a third operation configured to compute a second parameter associated with the feedforward pass, to update the second parameter based on the calculated gradients during the backpropagation pass, and to export the second parameter from the first portion of the artificial intelligence model to the second portion of the artificial intelligence model;
• and
• a fourth operation configured to compute an activation function during the feedforward pass and to send its gradient to the third operation during the backpropagation pass.

A computer program product stored on a non-transitory computer-readable medium and comprising machine executable instructions, the machine executable instructions, when executed, causing a processing device to:

• execute a first portion of an artificial intelligence model within a trusted execution area of an information processing system and a second portion of the artificial intelligence model within an untrusted execution area of the information processing system, wherein data at least one of obtained and processed in the first portion of the artificial intelligence model is inaccessible to the second portion of the artificial intelligence model;
• wherein a plurality of operations, customizable in accordance with one or more interfaces of a deep learning framework and executed by the information processing system, each comprise one part executable in the trusted execution area and another part executable in the untrusted execution area, and wherein the one part and the other part of each of the plurality of operations communicate via one or more calls between the trusted execution area and the untrusted execution area;
• wherein the plurality of operations is computed with a feedforward pass prior to calculating gradients and updating parameters of the plurality of operations based on the computed plurality of operations in a backpropagation pass;
• and
• wherein the plurality of operations comprise:
• a first operation configured to assign the at least one of obtained and processed data into a first layer of the trusted execution area;
• a second operation configured to compute a first parameter associated with the feedforward pass, to update the first parameter based on the calculated gradients during the backpropagation pass, and to export the first parameter from the first portion of the artificial intelligence model to the second portion of the artificial intelligence model;
• a third operation configured to compute a second parameter associated with the feedforward pass, to update the second parameter based on the calculated gradients during the backpropagation pass, and to export the second parameter from the first portion of the artificial intelligence model to the second portion of the artificial intelligence model;
• and
• a fourth operation configured to compute an activation function during the feedforward pass and to send its gradient to the third operation during the backpropagation pass.