Reasoning Graphs for Secure Insight Dissemination
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Solution Overview
Problem
Handling uniquely identifiable data poses challenges due to regulatory requirements and the risk of unwanted disclosure, especially when communicating insights derived from such data outside the hosting entity, which necessitates secure and protected methods for data protection and encryption.
Innovation Solution
The use of reasoning graphs to determine insights without exposing uniquely identifiable data, where a universally unique identifier (UUID) encodes the insight identifier, allowing only the reasoning functions and discrete decisions to be disclosed, breaking any direct correlation between the reasoning path and the data, thus ensuring secure dissemination of insights without revealing the underlying data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If uniquely identifiable data is disclosed to communicate insights, then complete transparency of reasoning is achieved, but data security and regulatory compliance deteriorate
Solution Approach 1:
The patent segments the reasoning process into discrete, independently verifiable components (reasoning steps, evidence sources, decision criteria) that can be disclosed without exposing the underlying uniquely identifiable data. This allows transparency of the reasoning logic while maintaining data security by separating the reasoning structure from the sensitive data itself.
Solution Approach 2:
The patent introduces an intermediary representation layer (structured reasoning graphs, anonymized data models, aggregated statistics) that mediates between the uniquely identifiable data and the disclosed insights. This intermediary layer preserves the essential reasoning logic while removing direct links to identifiable individuals or entities, thus maintaining both transparency and security.
2Loss of information
If human interaction with uniquely identifiable data is increased to explain reasoning, then transparency is improved, but the risk of unwanted disclosure increases
Solution Approach 1:
The patent implements self-service mechanisms where the system automatically generates explanations, summaries, and reasoning traces without requiring human staff to directly access or handle the uniquely identifiable data. Automated natural language generation, algorithmic reasoning validation, and machine-mediated explanation systems provide transparency while eliminating human exposure to sensitive data.
Solution Approach 2:
The patent replaces manual human review and explanation processes with automated computational systems that can analyze, explain, and validate reasoning logic without human intervention. This substitution of mechanical/automated systems for human interaction maintains transparency while ensuring data protection integrity by eliminating human access points.
3Loss of information
If reasoning paths are disclosed to show how insights are derived, then transparency is improved, but the ability to trace back to unique entities increases disclosure risk
Solution Approach 1:
The patent inverts the traditional approach by disclosing reasoning paths in reverse: instead of showing how specific data leads to insights, it shows how general reasoning patterns and aggregated evidence converge to produce insights. The reasoning graphs are constructed to flow from anonymous evidence and logical operations toward conclusions, making it impossible to trace back to unique entities while maintaining full transparency of the reasoning process.
Data Source
AI summary
Embodiments disclosed herein relate to methods and systems for disseminating reasoning supporting insights made with uniquely identifiable data without disclosing the uniquely identifiable data.