Database Sanitization via Annotation-Driven Schema Validation
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Solution Overview
Problem
Central databases face challenges in sanitizing sensitive data for testing and development purposes without impacting the original data or compromising security, as changes to source code can unpredictably alter or delete data, and sensitive information needs protection.
Innovation Solution
A central database system generates a sanitized copy by using annotation files to identify and protect sensitive data, creating a validated copy of each data table based on the database schema, allowing users to access and modify data without risking the original database's security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a sanitized copy of the database is generated for testing, then user safety and data protection are improved, but system complexity and resource consumption increase
Solution Approach 1:
The patent creates a sanitized copy of the database that mirrors the production database structure but contains masked or placeholder values for sensitive data. This copy allows testing activities to proceed without affecting the original production data, thereby improving data protection while managing system complexity through controlled replication.
Solution Approach 2:
The sanitization system acts as an intermediary layer between the production database and testing environments. It processes data requests, masks sensitive information, and provides sanitized data to testers, thereby protecting original data while enabling safe testing operations.
2Productivity
If the original database is accessed for testing, then testing efficiency is improved, but data security and integrity deteriorate
Solution Approach 1:
Instead of accessing the original production database directly for testing, the system provides sanitized copies with masked sensitive data. This maintains testing efficiency by allowing full database access patterns while eliminating security risks through data masking in the copy.
Solution Approach 2:
The database access is segmented into production data (for operational use) and sanitized test data (for development and testing). This segmentation allows testing activities to proceed efficiently without compromising production data security, as each segment serves its specific purpose with appropriate access controls.
3Reliability
If data is masked or redacted for testing, then data protection is improved, but data utility for testing purposes deteriorates
Solution Approach 1:
The sanitization process applies different masking strategies to different data fields based on their sensitivity levels. Non-sensitive fields retain their full values for testing, while only sensitive fields are masked. This local quality approach preserves data utility for testing purposes while protecting sensitive information.
Solution Approach 2:
The system changes data parameters (values) selectively - maintaining original values for non-sensitive fields and transforming sensitive field values into masked or placeholder representations. This parameter change approach allows testing to proceed with sufficient data utility while achieving the required protection level.
Data Source
AI summary
A central database system allows users to access and use data stored in a relational database. In order to ensure that the stored data is not detrimentally impacted and that the security of the stored data is maintained, the central database system generates a sanitized copy of the database. The central database system stores and accesses annotation files associated with data tables of the database and a schema identifying the structure of the database. Based on the schema, for each data table, the central database system validates the annotation file. A copy of the data table is created and sanitized corresponding to the annotation file. The sanitized copies of the data tables are used to generate a sanitized copy of the database. The sanitized copy of the database may then be accessed and used by users of the central database system without impacting the relational database.