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AssigneeInternational Business Machines Corporation
InventorsKanthi Sarpatwar, Nalini K. Ratha, Karthikeyan Shanmugam, Karthik Nandakumar, Sharathchandra Pankanti, Roman Vaculin
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A method, apparatus and computer program product for homomorphic inference on a decision tree (DT) model. In lieu of HE-based inferencing on the decision tree, the inferencing instead is performed on a neural network (NN), which acts as a surrogate. To this end, the neural network is trained to learn DT decision boundaries, preferably without using the original DT model data training points. During training, a random data set is applied to the DT, and expected outputs are recorded. This random data set and the expected outputs are then used to train the neural network such that the outputs of the neural network match the outputs expected from applying the original data set to the DT. Preferably, the neural network has low depth, just a few layers. HE-based inferencing on the decision tree is done using HE inferencing on the shallow neural network. The latter is computationally-efficient and is carried without the need for bootstrapping.
A method for privacy-preserving homomorphic inferencing with respect to a decision tree-based model, comprising:
An apparatus, comprising:
A computer program product in a non-transitory computer readable medium for use in a data processing system to provide privacy-preserving homomorphic inferencing with respect to a decision tree-based model, the computer program product holding computer program instructions that, when executed by the data processing system, are configured to:
A method of machine learning-as-a-service from a cloud computing infrastructure, comprising:
The method as described in claim 25 wherein the neural network is a shallow neural network that has an input layer, an output layer, and ≤three (3) hidden layers.
Dependencies are inferred from the claim text.