Trust Calculus for Edge Computing Verification
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Solution Overview
Problem
Edge computing environments face challenges in establishing and validating trust among multiple entities due to resource constraints and the need for secure, efficient management of trust relationships in multi-tenant, multi-owner settings, particularly in scenarios where latency and power consumption are critical.
Innovation Solution
The implementation of a trust calculus extended for edge computing settings, utilizing data definition languages like JSON, JWT, and CWT, to analyze and enforce trust properties through security controllers and orchestrators, ensuring secure communication and resource allocation across edge nodes, and integrating attestation operations for named function networks and information-centric networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If trust validation mechanisms are implemented in edge computing environments, then security and trustworthiness are improved, but device complexity and resource consumption increase
Solution Approach 1:
The patent introduces a trust calculus framework as an intermediary layer between edge computing entities. This framework provides standardized trust validation mechanisms that simplify complex trust relationships by defining formal rules for trust establishment, verification, and management. The trust calculus acts as a mediator that handles the complexity of multi-entity trust relationships without requiring each entity to implement complex validation logic independently.
Solution Approach 2:
The patent applies parameter changes by transforming trust relationships into quantifiable metrics and formal logical expressions. By converting qualitative trust concepts into measurable parameters that can be evaluated against defined thresholds and rules, the system enables automated trust validation while reducing the cognitive complexity of managing trust relationships across multiple entities.
2Reliability
If trust calculus operations are performed in resource-constrained edge devices, then trust validation capability is improved, but power consumption and processing overhead increase
Solution Approach 1:
The patent implements partial action by enabling trust validation operations to be performed selectively based on system capabilities and requirements. The trust calculus framework allows edge devices to perform only the necessary subset of trust validation operations appropriate to their resource constraints, rather than requiring full-featured trust management on all devices. This enables lightweight trust validation on resource-constrained devices while maintaining security.
Solution Approach 2:
The trust calculus framework segments trust validation into discrete, modular operations that can be independently executed. By breaking down complex trust validation into smaller computational units (such as individual rule evaluations, metric comparisons, and logical operations), the system enables resource-constrained devices to perform trust validation in manageable increments, reducing overall power consumption while maintaining validation capability.
3Adaptability or versatility
If multiple entities interact in edge computing environments, then service capability and functionality are improved, but trust management complexity and security risks increase
Solution Approach 1:
The patent implements universality by designing a trust calculus framework that provides a unified approach to trust management across diverse edge computing entities. The framework defines universal trust validation rules and mechanisms that can be applied consistently across different types of entities (devices, services, users, organizations), enabling simplified multi-entity interaction while maintaining security. This universal approach reduces the need for entity-specific trust management complexity.
Solution Approach 2:
The trust calculus framework serves as an intermediary that manages trust relationships between multiple entities without requiring direct complex interactions between each pair of entities. By introducing this formal framework as a mediator, the system enables multiple entities to interact through standardized trust validation protocols, reducing the overall complexity of trust relationship management while supporting diverse multi-entity scenarios.
Data Source
AI summary
Various aspects of methods, systems, and use cases for verification and attestation of operations in an edge computing environment are described, based on use of a trust calculus and established definitions of trustworthiness properties. In an example, an edge computing verification node is configured to: obtain a trust representation, corresponding to an edge computing feature, that is defined with a trust calculus and provided in a data definition language; receive, from an edge computing node, compute results and attestation evidence from the edge computing feature; attempt validation of the attestation evidence based on attestation properties defined by the trust representation; and communicate an indication of trustworthiness for the compute results, based on the validation of the attestation evidence. In further examples, the trust representation and validation is used in a named function network (NFN), for dynamic composition and execution of a function.