ML Model Execution Attestation for Trusted Edge Inference
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Solution Overview
Problem
Service providers face challenges in trusting the execution of machine learning models on untrusted edge computing devices due to concerns over data provenance, model integrity, and security, which can lead to privacy breaches, network lag, and increased costs.
Innovation Solution
Implementing a control plane architecture with an attestation module to verify the lineage of data and ML models, using hardware components and cryptographic controls to ensure secure execution and data integrity, while maintaining trust and control over the data processing pipeline.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If ML models are executed on untrusted edge computing devices, then network lag is reduced and processing speed is improved, but security and model integrity cannot be guaranteed
Solution Approach 1:
A control plane acts as an intermediary between the service provider and edge computing devices. It receives requests from service providers, verifies device credentials, and manages ML model deployment to edge devices. This mediator ensures that only authorized devices can execute models while maintaining security policies, thus resolving the contradiction between enabling edge execution for speed and ensuring model integrity through trusted execution.
2Productivity
If ML models are executed on untrusted edge computing devices, then processing capability is improved, but privacy breaches and security risks increase
Solution Approach 1:
The control plane implements feedback mechanisms by receiving execution results from edge devices and verifying them against expected outcomes. It monitors the execution environment and can revoke access or update security policies based on observed behavior. This continuous feedback loop enables the system to maintain high processing capability while detecting and preventing privacy breaches through active monitoring and verification.
3Reliability
If cloud-based services are used to execute ML models, then model control and security are maintained, but network lag increases and costs increase
Solution Approach 1:
The system segments the ML execution workload between cloud-based control plane and edge-based inference plane. The control plane retains model management, security policies, and verification functions in the cloud, while deploying model execution to edge devices for local processing. This segmentation allows model control and security to remain centralized while eliminating network lag for actual inference operations, as data processing occurs locally at the edge.
Data Source
AI summary
In an example, an apparatus is described. The apparatus comprises processing circuitry comprising a control module. The control module determines whether a computing device communicatively coupled to the control module is in a specified state for executing a machine learning model controlled by a third party entity. In response to determining that the computing device is in the specified state, the control module is to send, to an attestation module in a data processing pipeline associated with the computing device, an indication that the computing device is in the specified state.