Semantic Information Sharing Across Security Boundaries
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current information sharing systems are hindered by the need for manual human review to interpret semantics and make releasability decisions, which is slow and expensive, and existing automatic mechanisms cannot reliably process the meaning of information, leading to inefficiencies in sharing information across security boundaries.
Innovation Solution
A system and method that uses a server with a processor and memory to receive and process data structured as semantic statements, retrieve rules and ontology, and determine consistency with these rules and ontology, removing inconsistent facts and releasing data when consistent, leveraging an automatic theorem prover for mathematically provable decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual human review is used to interpret semantics and make releasability decisions, then reliability of decisions is improved, but productivity and speed of information sharing deteriorate
Solution Approach 1:
The patent introduces an automatic theorem prover as an intermediary between the data and the security policies. This mediator translates security policies into formal logical rules and automatically proves whether data satisfies these rules, eliminating the need for manual human review while maintaining high assurance of decision correctness.
Solution Approach 2:
The patent replaces the mechanical human review process with an automated computational system. The theorem prover mechanically applies logical rules to determine releasability, substituting human cognitive processes with automated logical deduction that is both fast and highly reliable.
2Productivity
If existing automatic mechanisms are used to process information, then productivity is improved, but reliability and accuracy of semantics processing deteriorate
Solution Approach 1:
The patent replaces conventional computational approaches that merely match strings and look for sensitive words with a formal theorem proving system. This substitution enables the automatic processing of semantic meaning through logical deduction, achieving both high speed and high accuracy in semantics interpretation.
Solution Approach 2:
The patent fundamentally changes the parameter of processing from superficial string matching to deep logical reasoning. By transforming security policy evaluation into formal logical proofs, the system achieves accurate semantics processing at automated speeds.
3Reliability
If human review is performed to release information, then reliability of security decisions is improved, but loss of time and operational cost increase
Solution Approach 1:
The patent enables the system to serve itself by automatically proving security policy compliance without human intervention. The theorem prover autonomously evaluates data against formalized policies and makes releasability decisions, eliminating time-consuming manual review while maintaining high assurance of correctness.
4Reliability
If information is labeled as more sensitive than it actually is, then security protection is improved, but information sharing capability deteriorates
Solution Approach 1:
The patent replaces conservative labeling approaches with automated theorem proving that precisely determines the actual sensitivity of information. By mechanically applying logical rules to evaluate data against policies, the system accurately identifies what can be shared without over-classification, increasing information sharing volume while maintaining appropriate security protection.
Data Source
AI summary
A system and method for sharing information across security boundaries including a server for sharing information between networks. The method for includes receiving data structured as semantic statements, retrieving a set of rules and ontology, processing a semantic statement from data with rules and ontology, determining whether the semantic statement is consistent with rules and ontology, and determining a fact that gives rise to an inconsistency if the semantic statement is inconsistent. The method further includes removing the fact that gives rise to the inconsistency and releasing data when the semantic statement is consistent with rules and ontology.