First-Packet Confidence Metadata for Data Stream Trust Assessment
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Solution Overview
Problem
Current data confidence fabrics lack a mechanism to classify data streams by both application type and the trustworthiness of the data source, with existing methods applying confidence metadata in a sidecar without altering the data stream.
Innovation Solution
Prepend confidence metadata, also known as trust metadata, to the data stream itself, allowing nodes within the data confidence fabric to assess the trustworthiness of the data and its source based on known attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If confidence metadata is applied in a sidecar without altering the data stream, then the data stream remains unchanged and simple, but nodes cannot immediately recognize the trustworthiness of the data stream
Solution Approach 1:
The patent merges confidence metadata directly into the data stream by prepending it to the first packet, combining what were previously separate entities (data stream and confidence metadata) into a single integrated structure. This allows nodes to immediately recognize trustworthiness without requiring separate sidecar mechanisms.
Solution Approach 2:
The confidence metadata is prepended to the first packet of the data stream before the data is processed by nodes. This preliminary action ensures that trustworthiness information is available immediately when the data stream arrives at nodes, eliminating the need for separate assessment mechanisms.
2Productivity
If confidence metadata is prepended to the data stream, then nodes can immediately assess trustworthiness, but the data stream structure becomes more complex
Solution Approach 1:
By performing the trust assessment information preparation in advance (prepending to the first packet), the system eliminates the need for time-consuming individual assessments at each node, thereby improving productivity despite the increased structural complexity.
Solution Approach 2:
The prepended confidence metadata serves multiple functions: it provides trustworthiness information, enables classification by application type, and facilitates immediate node assessment. This multi-functionality justifies the increased structural complexity by delivering multiple benefits from a single modification.
3Loss of information
If first packet decoding is used to classify data by application type, then data classification is enabled, but there is no mechanism to assess the trustworthiness of the data source
Solution Approach 1:
The patent combines application type classification and trustworthiness assessment into a single mechanism by including both types of information in the confidence metadata that is prepended to the first packet. This merging prevents loss of trustworthiness information while maintaining the classification capability.
Solution Approach 2:
The confidence metadata structure is designed to serve multiple purposes: it provides application type classification information and simultaneously provides trustworthiness assessment information. This multi-functionality eliminates the need for separate mechanisms and prevents information loss.
Data Source
AI summary
One example method includes receiving, at a node of a data confidence fabric, a data stream, prepending, by the node, a data confidence fabric header to the data stream so as to create a prepended data stream, and the data confidence fabric header includes confidence metadata relating to the data stream, and transmitting, by the node, the prepended data stream to another node of the data confidence fabric. The confidence metadata includes metadata about hardware and/or software associated with the data in the data stream.