Edge ML Model Protection via Control Plane Attestation
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Solution Overview
Problem
Service providers face challenges in controlling and securing machine learning (ML) models when executed on edge computing devices, including data integrity, intellectual property protection, and trust in the execution environment, due to concerns about data provenance, security, and potential attacks on ML models.
Innovation Solution
A system architecture with a control plane and data plane separation, utilizing a trusted control module and attestation module to manage and verify the execution of ML models on edge devices, ensuring data processing pipelines are secure and trustworthy, and providing cryptographic controls to protect model integrity and user data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If ML models are executed on edge computing devices, then network latency is reduced and processing speed is improved, but security and control over the ML models deteriorate
Solution Approach 1:
The system divides the execution environment into two segments: a trusted control plane that maintains security and model integrity, and an untrusted data plane that performs actual ML inference. This segmentation allows the model to run on edge devices for low latency while the control plane verifies model integrity and manages security policies.
Solution Approach 2:
A control plane acts as an intermediary between the service provider and the edge computing device. It verifies model integrity, manages model lifecycle, and enforces security policies without requiring the service provider to directly trust the edge device. The control plane mediates all interactions to maintain security while enabling edge execution.
2Loss of energy
If ML models are deployed to edge devices, then network costs are reduced, but control and verification of model execution deteriorate
Solution Approach 1:
The control plane continuously monitors and verifies model execution through feedback mechanisms. It receives status information from the data plane, verifies model integrity, and can update or revoke model access rights based on observed behavior. This feedback loop maintains control over distributed model deployments without requiring constant network communication for inference.
Solution Approach 2:
The control plane performs preliminary verification and validation of ML models before deploying them to edge devices. It verifies model integrity, checks compliance with security policies, and prepares model packages in advance. This preliminary action ensures that only authorized and intact models are deployed, reducing the need for continuous control overhead during inference.
3Adaptability or versatility
If ML models are executed on untrusted devices, then device versatility is improved, but trust in execution environment deteriorates
Solution Approach 1:
The system changes the trust model parameters by shifting from trusting the hardware environment to trusting the software control plane. Instead of requiring edge devices to be physically secure or controlled, the system uses cryptographic verification and control plane mediation to establish trust. This parameter change enables execution on diverse, untrusted devices while maintaining reliability through software-based trust verification.
Data Source
AI summary
In an example, an apparatus is described. The apparatus comprises processing circuitry comprising a control module. The control module is to protect information regarding a machine learning model owned by a third party. The information is protected in a memory communicatively coupled to the control module. In response to receiving an indication that a computing device under control of the control module complies with a third party policy associated with the machine learning model, the control module is to release the information to the computing device.