Model Data Transformation for Secure ML Distribution
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Solution Overview
Problem
Autonomous driving systems using machine learning models are susceptible to reverse engineering and unauthorized distribution.
Innovation Solution
Applying a transformation to data generated by a machine learning model and providing the transformed data to a consumer that is configured to reverse the transformation, ensuring the data can only be used by authorized systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If machine learning model data is distributed without transformation, then ease of operation is improved, but security and reliability deteriorate due to susceptibility to reverse engineering and unauthorized distribution
Solution Approach 1:
The patent applies a transformation to the machine learning model data before distribution. This preliminary action modifies the data structure or format in advance, so that when the data is distributed, it cannot be easily reverse-engineered or used unauthorizedly without the corresponding reverse transformation capability.
2Reliability
If a transformation is applied to machine learning model data, then security and reliability are improved, but device complexity increases due to the need for transformation and reverse transformation capabilities
Solution Approach 1:
The patent introduces a transformation as an intermediary layer between the original machine learning model data and its distributed form. This intermediary transformation layer protects the data while maintaining functionality, as the transformation and its reverse are designed to preserve the essential computational properties needed for model operation.
Data Source
AI summary
Transforming model data, including: applying a transformation to data generated by a machine learning model; and providing the transformed data to a consumer of the transformed data, wherein the consumer is configured to: reverse the transformation applied to the transformed data; and perform one or more operations based on the data.