US 12,216,758 B2
Training data protection in artificial intelligence model execution environment
Jinpeng Liu, Shanghai (CN); Michael Estrin, Austin, TX (US); and Zhen Jia, Shanghai (CN)
Assigned to EMC IP Holding Company LLC, Hopkinton, MA (US)
Filed by EMC IP Holding Company LLC, Hopkinton, MA (US)
Filed on Jun. 21, 2021, as Appl. No. 17/352,653.
Prior Publication US 2022/0405383 A1, Dec. 22, 2022
Int. Cl. G06F 21/53 (2013.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01)
CPC G06F 21/53 (2013.01) [G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06F 2221/033 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method, comprising:
executing 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;
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; and
wherein the information processing system comprises at least one processor and at least one memory storing computer program instructions wherein, when the at least one processor executes the computer program instructions, the information processing system performs the method.