Database System In-Memory Data Permutation for Security Analysis
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Solution Overview
Problem
Conventional methods for analyzing data to improve computer system security face performance penalties due to data copying onto external systems or networks, and inefficiencies in programming languages like Java, which also compromise security by exposing data to potential attacks.
Innovation Solution
A method that processes structured data directly within the database, using in-memory database systems and permutation operations to analyze access control data, reducing the need for external data transfer and utilizing efficient programming languages like C++ for parallel processing, while maintaining data security by minimizing code and avoiding network vulnerabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is sent to an external system for processing, then data analysis can be performed, but performance is degraded due to data copying and network transmission
Solution Approach 1:
The patent merges the data analysis functionality directly into the database system by implementing analysis operations within the database engine itself. This eliminates the need to export data to external systems, as the same database infrastructure that stores the data also processes the analysis queries, thereby removing copying and network transmission overhead.
Solution Approach 2:
The patent introduces an intermediary layer within the database system that handles analysis operations. Instead of direct communication between external applications and the database, an internal query processing mechanism serves as an intermediary, allowing analysis to be performed on data while it remains stored in the database, thus avoiding data export and import operations.
2Productivity
If data is copied to magnetic storage medium or sent over network, then external processing can be performed, but security is compromised due to external exposure
Solution Approach 1:
The patent extracts the harmful aspect of external data exposure by keeping the data entirely within the secure database boundary. Only the necessary query processing and result generation functions are implemented within the database engine, while the actual sensitive data never leaves the protected database environment, thus maintaining security while enabling analysis.
Solution Approach 2:
The database engine acts as an intermediary that processes analysis requests without exposing the underlying data to external systems. The intermediary layer translates external analysis requirements into internal database operations, ensuring that data remains protected within the database security perimeter while still allowing external applications to obtain analytical results.
3Speed
If application code accesses database memory for parallel processing, then analysis speed improves, but security and complexity increase
Solution Approach 1:
The patent merges the application logic with the database engine by implementing analysis operations as database-internal functions. This integration allows the database engine to directly utilize its own memory structures and processing pipelines for parallel operations, eliminating the need for complex inter-process memory sharing mechanisms while achieving high-speed processing.
Solution Approach 2:
The database engine is designed to perform multiple functions: it serves as both the data storage system and the data processing/analysis engine. This multi-functionality allows the same infrastructure to handle both transactional data operations and analytical processing, enabling parallel execution without requiring separate memory spaces or complex access protocols between different systems.
Data Source
AI summary
Structure data is received from a database. The structured data may comprise data values and specifying relationships among the data values. A method is provided to include receiving selections of operations to be performed on the structured data and an order of the operations. The operations may include at least one permutation operation. The method may include determining a graphically displayable data format based on the selections of operations. The method may include representing the structured data and the relationships among the data values in the graphically displayable data format. The method may comprise performing operations according to the selections, including, permuting entries of the graphically displayable data format. The permuting may comprise bringing the entries of the graphically displayable data format into a logical order while maintaining the relationships. The method may also include displaying the entries for analysis after performing the operations.