Graph Index Edge Privacy Filtering for Enterprise Data
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Solution Overview
Problem
Enterprise graph systems face challenges in providing accurate user behavior insights while maintaining privacy, as private data must be considered to avoid disclosure during graph queries, which can reveal sensitive information.
Innovation Solution
Implementing a graph index that stores relationships with Boolean values indicating edge privacy, and using user authentication to filter out private edges during queries, ensuring only public information is returned.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If enterprise graph queries return all relationship data to provide accurate user behavior insights, then measurement precision is improved, but private information disclosure risk increases
Solution Approach 1:
The patent extracts private edges from the graph query results by filtering them out before returning data to users. The system identifies edges marked as private and removes them from the query output, allowing public behavior patterns to be analyzed while protecting sensitive relationships. This extraction principle directly resolves the contradiction by separating public analytical value from private sensitive data.
Solution Approach 2:
The patent introduces an intermediary filtering mechanism that sits between the graph database and the user interface. This intermediary layer processes query results, applies privacy filters based on edge attributes, and returns sanitized data. The mediator enables accurate behavior analysis by allowing the system to work with complete graph data internally while presenting only public information to users.
2Object-affected harmful factors
If the system filters out private edges during graph queries to maintain privacy, then privacy protection is improved, but loss of information increases
Solution Approach 1:
The patent applies partial filtering by removing only the private portion of the data (private edges) while retaining all public information. Rather than filtering out entire relationships or user data, the system selectively filters only those specific edges marked as private, preserving maximum useful information. This partial action principle allows the system to protect privacy without unnecessarily discarding public behavior patterns and enterprise activity data.
Solution Approach 2:
The patent applies different quality treatments to different parts of the data structure. Public edges are included in full detail in query results, while private edges are filtered out. The system maintains local quality by preserving the完整性 of public relationship data while removing only the sensitive portions, ensuring that public behavior insights remain accurate and comprehensive.
3Measurement precision
If the system stores and processes complete graph data including private edges, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent applies preliminary action by marking edges as private or public during the initial data ingestion and graph construction phase. Rather than adding complex filtering logic during query processing, the system pre-tags edges with privacy attributes when they are created or imported. This preliminary classification simplifies subsequent query operations, as the filtering mechanism only needs to check pre-existing attributes rather than analyzing edge content in real-time.
Solution Approach 2:
The patent implements self-service filtering where the graph data structure itself carries the privacy information through edge attributes. The filtering mechanism leverages the self-contained nature of the graph database, using built-in query capabilities to filter based on edge properties. This self-service approach reduces external system complexity by allowing the database to handle privacy filtering internally without requiring complex external filtering infrastructure.
Data Source
AI summary
A computer-implemented method that monitors the activity of different nodes within a system as well as crowd sourcing activity. The computer-implemented method determines that a first node formed a relationship with a second node, generates an edge based on the relationship between the first node and the second node, stores the edge in a graph index and assigns a privacy setting to the edge based on the relationship between the first and second nodes.