Trust Weight Propagation for Data Processing Accuracy
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Solution Overview
Problem
Existing methods for indicating the trustworthiness of processed data often rely on assumptions that compromise accuracy and usefulness, especially when dealing with large datasets from diverse sources, leaving consumers uncertain about the reliability of the information.
Innovation Solution
A computer-implemented method assigns trust weights to data items and processing rules based on associated metadata, propagating trust information through data processing to provide an indication of the trustworthiness of the output, using a 'possible world' interpretation to account for variable trust levels in both data and rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If trust weights are assigned to data items and processing rules based on metadata, then the accuracy and transparency of data processing outcomes are enhanced, but the device complexity increases due to additional weighting units and trust propagation mechanisms
Solution Approach 1:
The patent introduces trust weights as intermediary elements that mediate between raw data items/processing rules and the final trustworthiness indication. These trust weights act as mediators that quantify and propagate trust information through the data processing pipeline, enabling accurate trust assessment without requiring complex direct analysis of data reliability
Solution Approach 2:
The patent replaces complex qualitative trust assessment mechanisms with a quantitative mathematical framework using trust weights and propagation rules. Instead of relying on subjective or complex mechanical verification processes, the system uses numerical weights and algebraic operations (multiplication, addition) to compute trustworthiness, simplifying the overall system while maintaining precision
2Reliability
If trust information is propagated through data processing using multiple trust weights, then the reliability of output data is improved, but the computational time and processing complexity increase
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing trust weights for data items and processing rules in metadata before the actual data processing occurs. This allows the trust propagation to be performed efficiently during data processing by simply retrieving and combining pre-computed weights, rather than performing complex trust analysis in real-time
Solution Approach 2:
The patent transforms the abstract concept of trustworthiness into quantifiable parameters (trust weights with numerical values). By changing trust from a qualitative attribute to a quantitative parameter, the system enables efficient mathematical operations (multiplication and addition of weights) that are computationally simple and fast, reducing processing time while maintaining reliability
Data Source
AI summary
A method indicates a trustworthiness of data processed in accordance with a processing rule. A first trust weight is assigned to a data item to be processed to provide a weighted data item, the first trust weight representing a level of trust in the data item. A trust value is selected from a set of data trust values, the selected trust value being representative of a determined level of trust in the data item. The selected trust value is defined as the first trust weight which is associated with the data item. The first trust weight is assigned to a processing rule to provide a weighted processing rule, the first trust weight representing a level of trust in the processing rule. The weighted data item is processed in accordance with the weighted processing rule to generate a data output and an indication of a trust level for the data output.