ML Model Authentication via Generated Identifier Pairs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Machine learning-based applications face challenges in protecting intellectual property due to the inability to prevent trained models from being stolen or modified, leading to resource wastage in verifying their authenticity.
Innovation Solution
An identifier platform generates and uses identifier pairs based on machine learning models to authenticate and verify their integrity, selecting appropriate models based on the model type and specifications, and storing these pairs in scalable data structures for efficient retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are made accessible for use, then productivity and application deployment are improved, but the risk of model theft and modification increases
Solution Approach 1:
The system performs preliminary actions by generating identifier pairs and embedding authentication mechanisms into models before they are deployed. This advance preparation enables automatic verification of model authenticity without impacting deployment speed, resolving the contradiction between rapid model availability and security assurance
2Reliability
If traditional verification methods are used to check model authenticity, then model security is improved, but computational resources are wasted due to inability to detect stolen models
Solution Approach 1:
The verification system operates autonomously by automatically generating identifier pairs, embedding them in models, and performing verification without human intervention. This self-service approach eliminates manual verification overhead and reduces computational waste by efficiently detecting stolen models through automated identifier matching
Solution Approach 2:
The patent replaces traditional manual or complex computational verification methods with a streamlined identifier-based verification system. By substituting mechanical verification processes with automated identifier pair matching, the system achieves both high security and low computational overhead
3Reliability
If identifier pairs are generated and stored for all models, then model authentication capability is improved, but data storage requirements increase
Solution Approach 1:
The system extracts only the essential authentication elements (identifier pairs) from the complete model data and stores separately only what is necessary for verification. This extraction approach maintains strong authentication capability while minimizing storage requirements by separating authentication data from the full model artifacts
Data Source
AI summary
A device receives a machine learning model, model data associated with the machine learning model, and identifier generation data. The identifier generation data includes data utilized to generate identifier pairs that may be used to authenticate the machine learning model. The device selects an identifier model, for generating the identifier pairs, based on the machine learning model, the model data, and the identifier generation data. The device processes the machine learning model, the model data, and the identifier generation data, with the selected identifier model, to generate the identifier pairs and identifier pair data. The device stores the identifier pairs and the identifier pair data in one or more data structures, and utilizes the identifier pairs to identify and provide authentication for the machine learning model.