Context-Aware Pagerank Vectors for Security Level Segmentation
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Solution Overview
Problem
Conventional pagerank algorithms can cause security leaks by transferring relevance values across different security levels, leading to unauthorized information exposure, as they do not effectively manage relevance distribution across classified and unclassified content.
Innovation Solution
A context-aware pagerank system that generates pagerank vectors based on multiple search contexts and security levels, using bleed factors to adjust relevance scores, ensuring that relevance values are contained within secure compartments and do not leak to lower security levels, thereby maintaining data privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional pagerank algorithms transfer relevance values across different security levels, then the pagerank computation is simple and efficient, but security leaks occur and unauthorized information exposure happens
Solution Approach 1:
The pagerank vector is segmented into multiple context-specific vectors, where each vector corresponds to a specific security level or search context. This segmentation prevents relevance values from leaking across security boundaries while maintaining computational efficiency within each context. The system generates separate pagerank vectors for different contexts (e.g., classified, unclassified, confidential) rather than using a single unified vector.
Solution Approach 2:
The system applies different quality standards and computation rules to different segments of the pagerank system based on their security levels. Each context-specific pagerank vector is computed and adjusted according to its specific security requirements, allowing the system to maintain high security for classified content while preserving standard pagerank functionality for unclassified content.
2Reliability
If relevance values are accumulated across all security levels, then the pagerank computation is straightforward, but classified content inadvertently boosts unclassified documents
Solution Approach 1:
The accumulation process is segmented by security context, with separate accumulation operations for each security level. Relevance values are accumulated within each context-specific pagerank vector independently, preventing cross-level contamination while maintaining accumulation efficiency within permitted boundaries.
Solution Approach 2:
The system introduces context indicators and security level markers as intermediaries between different security levels. These intermediaries control the flow of relevance values, allowing accumulation only between appropriate security levels while blocking inappropriate cross-level accumulation that would cause security leaks.
3Reliability
If a single pagerank vector is used for all security levels, then the system is simple to implement, but security contexts are not preserved and information leaks occur
Solution Approach 1:
The single pagerank vector is divided into multiple context-specific vectors, each preserving its own security context. This segmentation maintains security boundaries while the overall system architecture remains unified and manageable, with each vector being processed independently according to its security requirements.
Solution Approach 2:
The multi-context pagerank system maintains a universal interface and processing framework that handles multiple security contexts through a unified architecture. The system can process different security levels using the same fundamental pagerank algorithm, reducing the perceived complexity while preserving security distinctions.
Data Source
AI summary
System and method for generating context-aware pageranks according to certain embodiments. For example, a method includes: generating a plurality of vector elements of a pagerank vector for a page based on a plurality of search contexts, each vector element of the plurality of vector elements corresponding to a respective search context, each search context of the plurality of search context corresponding to a respective security level of a plurality of security levels; obtaining one or more bleed factors for the plurality of search contexts, each bleed factor of the one or more bleed factors associated with two search contexts of the plurality of search contexts having two adjacent security levels of the plurality of security levels; and adjusting at least one vector element of the plurality of vector elements based on the one or more bleed factors.