Trust Broker Engine for Data Confidence Fabric
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Solution Overview
Problem
Computing networks lack mechanisms to ascertain and improve the trustworthiness of data, making it difficult for applications to rely on the data generated within these networks, as entities are unaware of existing trust improvement mechanisms and lack visibility into how to implement them.
Innovation Solution
A data confidence fabric (DCF) system with a trust broker engine that evaluates and improves trust scores by identifying and implementing trust insertion technologies, such as hardware root of trust, digital signatures, and immutable storage, to associate confidence scores with data flowing through the network, allowing applications to understand and exploit the trustworthiness of the data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If trust insertion technologies are deployed in the network, then data trustworthiness is improved, but entities remain unaware of these mechanisms and unable to benefit from them
Solution Approach 1:
The trust broker engine continuously monitors the computing network, automatically discovers trust insertion technologies, and provides feedback to entities about available trust mechanisms. This feedback loop enables entities to understand and benefit from trust technologies without manual configuration, resolving the contradiction between deploying trust technologies and maintaining visibility into them.
Solution Approach 2:
The system enables self-service by allowing the trust broker engine to autonomously identify, evaluate, and implement trust insertion technologies in the network without requiring entity intervention. Entities automatically benefit from trust mechanisms through the engine's self-directed operations, eliminating the information gap while maintaining improved data trustworthiness.
2Ease of operation
If applications lack visibility into trust mechanisms, then deployment is simple, but applications cannot make informed decisions about data reliability
Solution Approach 1:
The trust broker engine acts as an intermediary between trust insertion technologies and applications. It automatically discovers and evaluates trust mechanisms, then presents this information to applications in an accessible format. This intermediary role maintains deployment simplicity while providing applications with the trust information needed for informed decision-making about data reliability.
3Reliability
If entities activate trust insertion technologies, then network trustworthiness is improved, but costs and performance impacts are not understood
Solution Approach 1:
The trust broker engine provides comprehensive feedback to entities about the costs and performance impacts of activating trust insertion technologies. This feedback includes quantitative measurements that allow entities to understand the trade-offs involved, enabling informed decisions while still achieving improved network trustworthiness through technology activation.
4Measurement precision
If manual configuration of trust mechanisms is required, then control is precise, but implementation complexity increases significantly
Solution Approach 1:
The trust broker engine performs self-service by automatically discovering, evaluating, and implementing trust insertion technologies without requiring manual configuration. This automation maintains precise trust evaluation through the engine's systematic assessment methods while dramatically reducing implementation complexity by eliminating manual setup requirements.
Data Source
AI summary
A trust broker is disclosed for a data confidence fabric. The trust broker evaluates the trustworthiness of data flowing through a network that includes a data confidence fabric. The trust broker evaluates a baseline confidence score and generates a workorder to improve the baseline confidence score in a measurable way. The trust broker may implement the workorder and ensure that the trust improves in the data confidence fabric.