System and method for mimicking a neural network without access to the original training dataset or the target model

A system for performing machine learning to generate a new model to mimic a pre-trained target model without obtaining the pre-trained target model or its original training dataset, the system comprising:

• one or more processors configured to:
• send a set of random or semi-random input data to a remote device to randomly probe the pre-trained target model remotely by inputting the set of random or semi-random input data into the pre-trained target model,
• receive from the remote device a set of corresponding output data generated by applying the pre-trained target model to the set of random or semi-random input data,
• generate a random probe training dataset comprising the set of random or semi-random input data and corresponding output data generated by randomly probing the pre-trained target model,
• train a new model with the random probe training dataset so that the new model generates substantially the same corresponding output data in response to said input data to mimic the pre-trained target model, and
• remove a correlation in the new model based on training data linking an input to an output, without accessing at least one of the input or output, by adding to the random probe training dataset a plurality of random correlations to the output or input, respectively, to weaken or eliminate the correlation between the input and output.

A non-transitory computer-readable medium comprising instructions which, when implemented in one or more processors in a computing device, cause the one or more processors to mimic a pre-trained target model at a device without access to the pre-trained target model or its original training dataset by:

• sending a set of random or semi-random input data to a remote device to randomly probe the pre-trained target model remotely by inputting the set of random or semi-random input data into the pre-trained target model;
• receiving from the remote device a set of corresponding output data generated by applying the pre-trained target model to the set of random or semi-random input data;
• generating a random probe training dataset comprising the set of random or semi-random input data and corresponding output data generated by randomly probing the pre-trained target model;
• training a new model with the random probe training dataset so that the new model generates substantially the same corresponding output data in response to said input data to mimic the pre-trained target model;
• and
• removing a correlation in the new model based on training data linking an input to an output, without accessing at least one of the input or output, by adding to the random probe training dataset a plurality of random correlations to the output or input, respectively, to weaken or eliminate the correlation between the input and output.