Neural Network Parameter Conversion for Encrypted Prediction
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Solution Overview
Problem
Existing prediction model conversion methods, such as SecureML, CryptoNets, and MOBIUS, face issues with low prediction accuracy and high computational complexity, making them impractical for secure neural network processing.
Innovation Solution
A prediction model conversion method that converts neural network parameters for homogenization processing into parameters capable of performing nonlinear processing, generating an encrypted model that maintains input secrecy during prediction processing, thereby reducing computational overhead and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing prediction model conversion methods (SecureML, CryptoNets, MOBIUS) are used to perform prediction processing while keeping data secret, then privacy protection is achieved, but prediction accuracy significantly decreases and computational complexity increases
Solution Approach 1:
The patent converts multiple homogenization parameters into a single parameter that performs both homogenization and nonlinear processing. This parameter transformation enables the encrypted prediction model to maintain higher prediction accuracy by reducing the complexity of operations on encrypted data, while still preserving privacy through encryption.
2Reliability
If existing prediction model conversion methods (SecureML, CryptoNets, MOBIUS) are used to perform prediction processing while keeping data secret, then privacy protection is achieved, but computational complexity increases
Solution Approach 1:
The patent merges multiple homogenization parameters into a single parameter that performs both homogenization and nonlinear processing functions. This consolidation reduces the number of computational operations required in the encrypted prediction model, thereby decreasing computational complexity while maintaining privacy protection through encryption.
3Reliability
If multiple parameters for homogenization processing are used in the prediction model, then processing completeness is achieved, but the amount of computation increases
Solution Approach 1:
The patent transforms multiple homogenization parameters into a single parameter that performs both homogenization and nonlinear processing. This parameter change reduces the computational burden and increases processing speed, while the converted parameter maintains the necessary processing completeness for accurate predictions in the encrypted domain.
Data Source
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AI summary
A prediction model conversion method includes: converting a prediction model by converting at least one parameter which is included in the prediction model and is for performing homogenization processing into at least one parameter for performing processing including nonlinear processing, the prediction model being a neural network (S001); and generating an encrypted prediction model that performs prediction processing with input in a secret state remaining secret by encrypting the prediction model that has been converted (S002).