Trust Score Recalculation for Peer Transfer Investigation
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Solution Overview
Problem
Existing systems struggle to accurately determine trustworthiness based on peer-to-peer interactions due to the vast amount of data and limited information available, which can lead to biased judgments and difficulty in analyzing relationships between individuals.
Innovation Solution
A system that logs and tracks peer-to-peer transactions, predicts related interactions, and calculates confidence scores using clustering algorithms to provide objective metrics, allowing users to adjust data points and transfer trust determinations across interconnected individuals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a system logs and tracks peer-to-peer interactions to provide objective information, then trust judgment accuracy is improved, but data complexity and analysis difficulty increase
Solution Approach 1:
The patent introduces a trust score system as an intermediary that mediates between raw peer-to-peer interaction data and trust judgments. The system calculates trust scores based on logged interactions and presents these scores as simplified metrics, acting as a mediator that translates complex interaction data into actionable trust indicators without requiring users to directly analyze the underlying data complexity
Solution Approach 2:
The system transforms the complex multi-dimensional peer-to-peer interaction data into a simplified trust score parameter. By changing the representation from raw interaction logs to calculated trust scores, the system maintains measurement precision while reducing the complexity of data analysis for end users
2Measurement precision
If the system analyzes all peer-to-peer interaction data, then trust score accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system extracts only the most relevant features from peer-to-peer interaction data that contribute to trust score calculation. Rather than processing all available data, the system identifies and extracts key interaction patterns and metrics, reducing processing time while maintaining trust score accuracy by focusing on the most informative data elements
Solution Approach 2:
The system implements partial action by calculating trust scores based on a representative subset of interaction data rather than exhaustive analysis of all interactions. This approach provides sufficiently accurate trust scores in a timely manner, accepting that complete analysis of every interaction would be excessive for the intended application
3Measurement precision
If the system provides detailed information about peer interactions, then trust assessment accuracy is improved, but user privacy is compromised
Solution Approach 1:
The trust score acts as an intermediary that conveys trust assessment information without revealing underlying personal interaction details. The system mediates between detailed interaction data and user privacy by presenting aggregated trust metrics that maintain assessment accuracy while protecting individual privacy through aggregation and anonymization
Solution Approach 2:
The system applies different levels of information disclosure to different users and contexts. Detailed interaction information is maintained in the system for accurate trust assessment, but only aggregated trust scores are presented to general users, while more detailed information is selectively disclosed only when necessary and appropriate, creating local quality variations in information transparency
Data Source
AI summary
A system and method for investigating trust scores. A trust score is calculated based on peer transfers, a graphical user interface displays actuatable elements associated with a first peer transfer from the peer transfers, in response to receiving an indication the first actuatable element has been actuated, recalculating the trust score without the first peer transfer.