Private Function Evaluation Using Machine Learning
Summary
USPTO published patent application US20260099704A1 for private function evaluation using machine learning. The invention covers systems and methods for training neural networks to approximate functions, generating shallow neural networks, and using secret sharing to enable private function evaluation without exposing inputs or function parameters.
What changed
USPTO published patent application US20260099704A1 for private function evaluation using machine learning filed by inventor Jonas Boehler. The application covers methods for training neural networks to approximate functions, generating shallow neural networks from trained networks, and using secret sharing techniques to enable evaluation of functions without exposing input parameters or function parameters to the evaluating entities. The filing date was October 4, 2024, with application number 18907046.
Technology companies developing machine learning applications involving sensitive data may have potential licensing interests in this private evaluation approach. The patent application provides insight into emerging privacy-preserving computation techniques using neural network approximations and secret sharing protocols.
Archived snapshot
Apr 18, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
PRIVATE FUNCTION EVALUATION USING MACHINE LEARNING
Application US20260099704A1 Kind: A1 Apr 09, 2026
Inventors
Jonas Boehler
Abstract
The present disclosure involves systems, software, and computer implemented methods for private function evaluation. One example method includes identifying a function provided by a function-providing entity. A neural network is trained to approximate the function. A shallow neural network is generated from the neural network. The shallow neural network approximates the function and includes shallow network parameters. The shallow network parameters are secret shared to a first set of entities. A request is received to execute the function using at least one input parameter. The input parameters are secret to the first set of entities. Secret-shared function outputs are received that are generated by the first set of entities using a set of secret-shared input parameters and the shallow neural network with secret-shared shallow network parameters. A function output is generated for the function using the secret-shared function outputs.
CPC Classifications
G06N 3/08 G06N 3/045
Filing Date
2024-10-04
Application No.
18907046
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