Machine Learning Model Protection via Software Implementation
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Solution Overview
Problem
Machine learning models are highly susceptible to algorithmic attacks, including static and dynamic attacks, which can lead to vulnerabilities such as malware embedding and reverse engineering, especially in safety-critical applications like medical diagnostics and autonomous vehicles.
Innovation Solution
A method is developed to generate software that implements machine learning models by applying software protection techniques, such as fixed-point operations and security features, to enhance the protection of the models against attacks, ensuring their functionality and secrecy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If machine learning models are stored as parameter files for easy loading and execution, then ease of operation is improved, but vulnerability to algorithmic attacks and reverse engineering increases
Solution Approach 1:
The patent creates a software implementation that copies the functionality of the ML model while protecting the actual parameters. The protected software reproduces the model's behavior without exposing the original parameter file, thereby maintaining operational ease while reducing vulnerability to attacks.
Solution Approach 2:
The patent introduces protected software as an intermediary layer between the parameter file and the execution environment. This intermediary protects the parameters by embedding them within secured software code, preventing direct access and analysis while maintaining the model's functional capabilities.
2Reliability
If machine learning models are protected through software implementation, then security against attacks is improved, but device complexity increases
Solution Approach 1:
The patent transforms the model parameters into protected software implementations with modified structural parameters. By changing the representation format from standalone parameter files to embedded software code, the system achieves enhanced security while managing complexity through systematic transformation processes.
Solution Approach 2:
The patent divides the protection mechanism into distinct segments: parameter extraction, software generation, and protection application. This segmentation allows each component to be optimized independently, reducing overall system complexity while maintaining comprehensive security protection.
3Object-affected harmful factors
If software protection techniques are applied to machine learning models, then resistance to reverse engineering is improved, but manufacturing precision requirements increase
Solution Approach 1:
The patent performs preliminary actions by extracting parameters and generating protected software before deployment. This advance preparation ensures that the protection mechanism is built-in from the start, maintaining precision requirements while achieving robust resistance to reverse engineering attacks.
Solution Approach 2:
The patent replaces traditional mechanical parameter storage with software-based protection mechanisms. By substituting the physical parameter file structure with compiled software code, the system achieves higher resistance to analysis while managing precision through software-based transformation rather than physical manipulation.
Data Source
AI summary
A machine learning model protection method comprising: generating, based on a set of parameters that define a machine learning model, an item of software which, when executed by one or more processors, provides an implementation for the machine learning model; and applying one or more software protection techniques to the item of software.