Database Sanitizing Mechanism for Privacy and Integrity
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Solution Overview
Problem
Conventional methods lack an efficient way to purge sensitive information from databases without disrupting the structure and relations of the data, making manual updates time-consuming and resource-intensive, especially for large databases, and synthetically generated test databases often fail to accurately replicate real-world scenarios.
Innovation Solution
A generic method and mechanism for removing sensitive data from databases by generating benign values correlated to the relations, maintaining data integrity and structure, and using these values to create a sanitized database for testing and research purposes, which can be applied to any database system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If manual identification and overwriting of sensitive data is performed, then data privacy is protected, but the process is time-consuming and resource-intensive
Solution Approach 1:
The system automatically identifies sensitive data patterns and generates sanitization scripts without requiring manual intervention. The database management system self-performs the sanitization process by executing generated scripts, eliminating the need for manual identification and overwriting of sensitive data while maintaining privacy protection
Solution Approach 2:
The patent replaces manual mechanical processes with automated computer-based systems. The database management system uses automated pattern recognition and script generation mechanisms to identify and sanitize sensitive data, substituting the manual mechanical process of data identification and overwriting with automated electronic processes
2Object-affected harmful factors
If conventional manual updates are performed on database fields, then sensitive information is removed, but the structure and relations of the data are disrupted
Solution Approach 1:
The system performs preliminary identification of sensitive data patterns and generates sanitization scripts before actual data removal. This preliminary action allows the system to understand the data structure and relations beforehand, enabling subsequent sanitization to maintain structural integrity while removing sensitive information
Solution Approach 2:
The system executes generated scripts and monitors the results to ensure data structure integrity is maintained. Feedback mechanisms allow the system to verify that sanitization operations preserve relational constraints and structural relationships, enabling corrective actions if integrity is compromised
3Object-affected harmful factors
If synthetically generated test databases are created, then data privacy is protected, but the data patterns do not accurately replicate real-world scenarios
Solution Approach 1:
The system creates copies of actual database data patterns while removing sensitive information. By copying the structural patterns, relationships, and data distributions from real-world databases, the system generates test databases that accurately replicate real-world scenarios while protecting privacy through the removal of actual sensitive data values
Solution Approach 2:
The system changes the values of sensitive data parameters while preserving the structural parameters of the database. By maintaining data types, relationships, and pattern distributions while changing sensitive values to anonymized equivalents, the system preserves real-world accuracy while achieving privacy protection
4Object-affected harmful factors
If large databases with many entries are sanitized manually, then complete data purification is achieved, but the process becomes unwieldy and resource-intensive
Solution Approach 1:
The system segments the large database into manageable units by identifying and processing specific data patterns and relationships. This segmentation allows the sanitization process to handle large databases systematically through automated pattern-based processing rather than attempting to process the entire database as a single unwieldy unit
Solution Approach 2:
The system creates a universal sanitization mechanism that can handle various data types, patterns, and database structures through a single automated framework. The pattern-based approach provides multi-functionality, allowing the same system to sanitize different types of sensitive data across various database schemas without requiring separate manual processes for each data type
Data Source
AI summary
A method of generating a test database from a deployed database by “sanitizing”, or removing sensitive data, is employed for recreating problem scenarios from a customer site, verifying compatibility of customer data with database version updates, and for performance testing using actual, rather than generated, database structures. In database management operations, in can be beneficial to use data generated from actual operational scenarios rather than artificially generated data created from a test pattern. Data generated from actual operation, such has at a customer site, assures compatibility with the relation patterns and record volumes employed by the customer (user). However, databases often contain sensitive information that would be inappropriate, illegal, or vulnerable in a testing environment. Accordingly, the generated test database overwrites sensitive data values with benign, or generic values while preserving the structure and relations of the data stored.