Blockchain Zero Knowledge Model Validation
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Solution Overview
Problem
There is a need for a method to validate the accuracy and genuineness of computational models without sharing real data sets or model details, especially when models are obtained from third-party developers, while ensuring compliance with laws and regulations.
Innovation Solution
A system using blockchain and zero knowledge principles generates a synthetic data set that approximates real data, allowing validation of computational models by comparing results on a blockchain without transmitting real data or model information, ensuring immutability and protection of sensitive information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real data sets and model details are shared for validation, then validation accuracy is improved, but data security and model protection are compromised
Solution Approach 1:
The patent creates synthetic data sets that replicate the statistical properties and patterns of real data without containing actual sensitive information. These synthetic copies enable model validation while protecting the security and privacy of real data sets, resolving the contradiction between validation accuracy and data security
Solution Approach 2:
The patent introduces blockchain technology as an intermediary platform that facilitates model validation by providing a decentralized, immutable environment where synthetic data and validation results can be exchanged without requiring direct sharing of sensitive real data or model details between parties
2Reliability
If model details are made available for validation, then validation thoroughness is improved, but model intellectual property is compromised
Solution Approach 1:
The patent uses synthetic data sets as copies that preserve the essential characteristics needed for validation without revealing the actual model architecture, training data, or proprietary algorithms. This allows thorough validation while maintaining intellectual property protection
3Object-affected harmful factors
If synthetic data sets are used instead of real data, then data protection is improved, but validation realism may be compromised
Solution Approach 1:
The patent generates synthetic data sets with carefully controlled parameters that match the statistical properties, distributions, and relationships of real data. By adjusting and matching these parameters, the synthetic data maintains sufficient realism for valid model testing while providing inherent data protection
Data Source
AI summary
A method for determining the validity of a computational model using a blockchain and zero knowledge principles includes: storing, in a memory of a first computing system, a computational model; receiving, by a receiver of the first computing system, a blockchain data value from one block of a plurality of blocks comprising a blockchain, wherein the blockchain data value includes a data set; receiving, by the receiver of the first computing system, an expected accuracy value; applying, by a processor of the first computing system, the data set to the computational model to generate a result value; and determining, by the processor of the first computing system, a validity measurement for the computational model based on a comparison of the generated result value and the expected accuracy value.