Microsoft patents method to scramble AI math to hide data patterns

1. A method for obfuscating execution of operations of a machine-learning model during execution of inference tasks, the method comprising: receiving, by a processor, an inference task comprising input data to be processed by the machine-learning model, the inference task received from a requestor; creating, by the processor, a computational graph mapping execution dependency of the machine-learning model by representing input-dependent operations as nodes and specifying data flow paths between nodes as edges, the computational graph created based on a model architecture of the machine-learning model; identifying, by the processor, and based upon the computational graph, independent operations within the machine-learning model that are performed during the inference task that can be processed in a different order without changing an output of the inference task; generating, by the processor, using a pseudo-random number generator, randomization parameters for determining an execution order of the independent operations within the machine-learning model that are performed during the inference task; ordering, by the processor, the independent operations within the machine-learning model that are performed during the inference task according to the randomization parameters to create a reordered independent operations of the independent operations within the machine-learning model that are performed during the inference task; executing, by the processor, the reordered independent operations on the input data to produce an output of the inference task, the reordered independent operations generating randomized execution traces for each inference task of the machine-learning model; and transmitting, by the processor, the output of the inference task to the requestor.