Data Confidence Fabric Policy Scoring

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Solution Overview

Problem

Existing systems lack an efficient method to generate policy-based data confidence scores, which are crucial for determining the trustworthiness of data across various applications and environments.

Innovation Solution

A data confidence fabric (DCF) system that routes and scores data using policy-based confidence scores, where annotations from trust insertion technologies are weighted according to predefined policies to generate a final confidence score.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If policy-based weighting of trust insertions is implemented, then data trustworthiness assessment accuracy is improved, but system complexity increases

Engineering Contradiction:

Improvedata trustworthiness assessment accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system segments the trust assessment process into distinct components: multiple trust insertion technologies (blockchain, digital signatures, encryption) are applied independently to data, each generating separate annotations. These segmented trust indicators are then individually weighted according to policy before being aggregated into a final confidence score, allowing precise control over each trust mechanism's contribution.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system implements dynamic weighting through policy configurations that can adjust the importance of different trust insertion technologies based on context. The weighting factors are not fixed but can be modified through policy updates, allowing the system to adapt to different data types, sources, and security requirements without changing the underlying architecture.

Inventive Principle:

Principle #15Dynamics

2Reliability

If multiple trust insertion technologies are applied, then data confidence score reliability is improved, but processing time increases

Engineering Contradiction:

Improvedata confidence score reliabilityVSAvoidprocessing time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by pre-configuring policies that define weighting factors for different trust insertion technologies before data processing begins. These policies are established in advance and stored for quick retrieval, eliminating the need for complex real-time decision-making about which trust mechanisms to apply and how to weight them during data processing.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements self-service through automated policy application and confidence score generation. Once policies are configured, the system automatically applies the appropriate trust insertion technologies and their corresponding weights to data without requiring manual intervention, reducing both processing time and human resource requirements while maintaining reliable assessment.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS12332872B2Data confidence fabric policy-based scoring

Publication Date: 2025.06.17 DELL PROD LP

AI summary

Generating policy-based confidence scores for data is disclosed. Data captured by a data confidence fabric is annotated when the data is created, mutated, transited or otherwise handled in the data confidence fabric. The annotations are weighted by a policy to generate policy-based confidence scores. The policy-based confidence scores are used in determining whether the data is sufficiently trusted for use by an application.