Privacy enhancing deep learning cloud service using a trusted execution environment

A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a data processing system, causes the data processing system to implement an enhanced privacy deep learning system framework that is configured to:

• receive from a client computing device, an encrypted first subnet model of a neural network, wherein the first subnet model is one partition of multiple partitions of the neural network;
• load the encrypted first subnet model into a trusted execution environment of the enhanced privacy deep learning system framework;
• decrypt the first subnet model within the trusted execution environment and execute the first subnet model within the trusted execution environment;
• receive encrypted input data from the client computing device;
• load the encrypted input data into the trusted execution environment;
• and
• decrypt and process the input data in the trusted execution environment using the first subnet model executing within the trusted execution environment, wherein the first subnet model is a FrontNet subnet model comprising an input layer of the neural network and one or more intermediate layers of the neural network model, and wherein the neural network comprises a second subnet model that is a BackNet subnet model comprising an output layer of the neural network that outputs result data, and one or more intermediate layers of the neural network model;
• and
• output the result data to a deep learning system to perform a classification operation to classify the encrypted input data into one of a plurality of predefined classes.

A system, comprising:

• at least one processor;
• and
• at least one memory coupled to the at least one processor, wherein the at least one memory comprises instructions which, when executed by the at least one processor, cause the at least one processor to implement an enhanced privacy deep learning system framework that is configured to:
• receive from a client computing device, an encrypted first subnet model of a neural network, wherein the first subnet model is one partition of multiple partitions of the neural network;
• load the encrypted first subnet model into a trusted execution environment of the enhanced privacy deep learning system framework;
• decrypt the first subnet model within the trusted execution environment and execute the first subnet model within the trusted execution environment;
• receive encrypted input data from the client computing device;
• load the encrypted input data into the trusted execution environment;
• and
• decrypt and process the input data in the trusted execution environment using the first subnet model executing within the trusted execution environment, wherein the first subnet model is a FrontNet subnet model comprising an input layer of the neural network and one or more intermediate layers of the neural network model, and wherein the neural network comprises a second subnet model that is a BackNet subnet model comprising an output layer of the neural network that outputs result data, and one or more intermediate layers of the neural network model;
• and
• output the result data to a deep learning system to perform a classification operation to classify the encrypted input data into one of a plurality of predefined classes.