US 11,816,575 B2
Verifiable deep learning training service
Zhongshu Gu, Ridgewood, NJ (US); Heqing Huang, Mahwah, NJ (US); Jialong Zhang, San Jose, CA (US); Dong Su, Sunnyvale, CA (US); Dimitrios Pendarakis, Westport, CT (US); and Ian M. Molloy, Chappaqua, NY (US)
Assigned to International Business Machines Corporation
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Sep. 7, 2018, as Appl. No. 16/124,657.
Prior Publication US 2020/0082270 A1, Mar. 12, 2020
Int. Cl. G06N 3/084 (2023.01); G06F 21/60 (2013.01)
CPC G06N 3/084 (2013.01) [G06F 21/602 (2013.01)] 19 Claims
OG exemplary drawing
 
1. A method, in a data processing system, comprising:
executing, by a deep learning training service framework, a FrontNet subnet model of a deep learning model in a trusted execution environment of the deep learning training service framework;
executing, by the deep learning training service framework, a BackNet subnet model of the deep learning model in the deep learning training service framework external to the trusted execution environment, wherein the FrontNet subnet model comprises a first predetermined number of consecutive layers of the deep learning model from an input layer of the deep learning model to an intermediate layer, and wherein the BackNet subnet model comprises a second predetermined number of consecutive layers of the deep learning model from a layer, subsequent to the intermediate layer, to an output layer of the deep learning model;
decrypting, by a security module executing within the trusted execution environment, one or more encrypted training datasets;
training, by training logic of the deep learning training service framework, the FrontNet subnet model and BackNet subnet model of the deep learning model based on the decrypted training datasets, wherein the FrontNet subnet model is trained within the trusted execution environment and provides intermediate representations to the BackNet subnet model which is trained external to the trusted execution environment using the intermediate representations;
releasing, by the deep learning training service framework, a trained deep learning model comprising a trained FrontNet subnet model and a trained BackNet subnet model, to one or more client computing devices; and
generating, by a fingerprint generation module executing within the trusted execution environment, one or more first fingerprint data structures for the one or more training datasets, wherein each first fingerprint data structure comprises a fingerprint that is a normalized feature embedding of a penultimate layer of the BackNet subnet model.