Artificial Neural Network Security Through HSM Component Reconstruction
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Solution Overview
Problem
Trained artificial neural networks (ANNs) are vulnerable to attacks such as theft or manipulation, especially when decentralized, leading to unauthorized access, tampering, and potential financial or reputational damage, with existing software security mechanisms failing to protect against physical tampering and incurring significant performance overhead.
Innovation Solution
Disintegrate ANNs into feature engineering data and coefficient data, storing these components in a Hardware Security Module (HSM) to protect against tampering, and reintegrate them upon use, with verification using hash-based checks to ensure integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the ANN is stored in a decentralized manner at edge computers, then accessibility and distributed processing are improved, but security and vulnerability to attacks deteriorate
Solution Approach 1:
The ANN model is segmented into multiple components (model weights, architecture configuration, training metadata) which are stored separately in the HSM. This segmentation prevents attackers from obtaining the complete model through single-point breaches, thereby maintaining security while enabling distributed access to specific model components.
Solution Approach 2:
The HSM acts as an intermediary between the decentralized edge computers and the ANN model components. It provides a secure vault that mediates access requests, ensuring that only authenticated systems can retrieve model components, thus resolving the contradiction between distributed accessibility and security.
2Reliability
If software security mechanisms are applied to protect the ANN, then security is improved, but performance overhead increases significantly
Solution Approach 1:
Security measures are performed in advance during model deployment. The ANN components are secured in the HSM with authentication mechanisms pre-configured. During inference, the pre-established secure connections and cached authentication tokens enable fast model retrieval without real-time security computation overhead, thus maintaining high performance while ensuring security.
3Speed
If the ANN is maintained as a complete model at edge computers, then processing speed is improved, but vulnerability to theft and tampering increases
Solution Approach 1:
The complete ANN model is segmented into multiple components stored in the HSM. During inference, only the necessary components are retrieved and assembled in memory, never stored完整地 on edge devices. This eliminates the risk of theft and tampering while maintaining processing speed through efficient component retrieval and in-memory assembly.
Solution Approach 2:
Different components of the ANN are stored with different security characteristics in the HSM. Model weights may be stored with higher security protection than architecture metadata, allowing differential security policies that protect critical components while enabling fast access to less sensitive information, thus balancing security and performance.
Data Source
Figure 1A~1B
Figure 2
Figure 3A~3B
AI summary
A device (600) includes a hardware security module (602); and a processor (604), different from the hardware security module (602); wherein the processor (604) is configured to receive from the hardware security module (602) feature engineering data and coefficient data for a reference artificial neural network; and reconstruct the reference artificial neural network based on the feature engineering data, the coefficient data, and a predefined algorithm.