Machine Learning Provenance Verification via Digital Fingerprinting
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Solution Overview
Problem
Existing technologies face challenges in accurately identifying and verifying the provenance of machine learning systems, including distinguishing between authentic and malicious versions, and determining the responsible owner and developer.
Innovation Solution
A technique is developed to generate a digital fingerprint for a machine learning system based on its header, metadata, and content, allowing for the verification of its provenance and comparison with other systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If statistical testing is used to compare two LLMs, then equality can be tested on a statistical basis, but surgical modifications can make models behave alike for most inputs while remaining fundamentally different
Solution Approach 1:
The patent segments the LLM comparison process into multiple independent components: structural analysis (architecture, layers, parameters), training data fingerprinting, and behavioral testing. This segmentation allows verification of multiple aspects simultaneously, making it harder for surgical modifications to go undetected while maintaining statistical rigor in each component.
Solution Approach 2:
The patent introduces digital fingerprints as an intermediary mechanism that captures unique characteristics of training data and model architecture. These fingerprints serve as mediators that bridge structural analysis and behavioral testing, providing a reliable provenance marker that cannot be easily replicated through surgical modifications alone.
2Measurement precision
If detailed structural and training data analysis is performed, then provenance can be accurately identified, but the complexity of verification increases
Solution Approach 1:
The patent performs preliminary actions by generating digital fingerprints during the model training and deployment process itself. These fingerprints capture training data characteristics and architectural features in advance, so that provenance verification later requires only comparing these pre-computed fingerprints rather than re-analyzing entire training datasets, thus reducing verification complexity.
Solution Approach 2:
The patent creates simplified copies of training data characteristics through digital fingerprints. Instead of storing and comparing entire training datasets (which would be extremely complex), the system creates compact fingerprint representations that retain the essential provenance information while dramatically reducing verification complexity.
Data Source
AI summary
A request to verify a provenance of a machine learning system is received. A digital fingerprint for the machine learning system is generated based on a header associated with the machine learning system, metadata associated with the machine learning system, and content associated with the machine learning system. The digital fingerprint includes a first value that corresponds to the header associated with the machine learning system, a second value that corresponds to the metadata associated with the machine learning system, and a third value that corresponds to the content associated with the machine learning system. It is determined that the first value that corresponds to the header associated with the machine learning system matches a stored value that corresponds to a header associated with a previously verified machine learning system. A notification that the machine learning system is a same machine learning system as the previously verified machine learning system is provided.