At-Source Data Masking Using Unique Identifiers In-Place
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Solution Overview
Problem
Existing data masking techniques face challenges such as longer masking cycles, data security issues, heavy resource costs, and redundant data processing, particularly in ensuring data integrity and compliance during outsourcing and testing phases.
Innovation Solution
A system and method for at-source masking of sensitive data that identifies unique identifiers in a production database, creates temporary tables with pseudo-columns, and applies predefined masking rules to update the database in-place, avoiding data movement and additional infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data masking techniques are used to mask sensitive data for outsourcing and testing, then data security is improved, but masking cycle time increases and resource costs increase
Solution Approach 1:
The patent applies preliminary action by pre-configuring masking rules and identifiers in the production database before data needs to be masked. Unique identifiers are assigned to sensitive data elements in advance, and masking rules are predefined, so when masking is needed, the system can quickly apply transformations without extensive processing time. This resolves the contradiction by preparing security measures beforehand, reducing actual masking cycle time while maintaining data security.
Solution Approach 2:
The patent uses copying by creating temporary tables that contain copies of the production data with masked values. Instead of masking data in place or creating complex transformed versions, the system copies data to temporary structures where masking is applied systematically. This approach maintains security while accelerating the masking process through efficient data replication and transformation techniques.
2Reliability
If traditional data masking techniques are used to mask sensitive data, then data security is improved, but resource costs increase
Solution Approach 1:
The patent implements self-service by enabling the production database to mask its own sensitive data using internally stored unique identifiers and predefined masking rules. The system uses existing database resources and structures to perform masking operations without requiring extensive external computing resources or additional infrastructure. This reduces resource costs while maintaining data security through self-contained masking capabilities.
Solution Approach 2:
The patent applies parameter changes by transforming data values using configurable masking parameters such as substitution values, encryption keys, and transformation rules. The system dynamically adjusts masking parameters based on data sensitivity levels and security requirements, optimizing resource usage while maintaining appropriate security measures. This allows flexible resource allocation and reduces overall masking costs.
3Adaptability or versatility
If data is moved to separate databases or flat files for masking, then masking flexibility is improved, but infrastructure complexity and resource costs increase
Solution Approach 1:
The patent merges the masking functionality directly into the production database by storing unique identifiers and masking rules within the same database structure. Instead of separating masking operations into external systems or flat files, the system combines data storage, identifier management, and masking logic within a unified database framework. This reduces infrastructure complexity while maintaining masking flexibility through integrated rule-based transformations.
Solution Approach 2:
The patent implements universality by designing a multi-functional database system that handles data storage, unique identifier assignment, rule configuration, and masking operations all within a single platform. The production database serves multiple purposes: storing sensitive data, managing masking rules, tracking unique identifiers, and executing masking transformations. This eliminates the need for separate infrastructure components while providing versatile masking capabilities across different data types and sensitivity levels.
4Reliability
If comprehensive data masking is applied to all data fields, then data security is improved, but data utility for testing decreases
Solution Approach 1:
The patent applies local quality by differentiating masking treatment across different data fields based on their sensitivity levels. Instead of uniformly masking all data, the system applies masking only to specific sensitive fields identified by unique identifiers, while leaving non-sensitive fields unchanged. This selective approach maintains data security for sensitive information while preserving data utility for testing by retaining usable values in non-sensitive fields. The masking rules are configured at the field level, allowing precise control over what is masked and what remains accessible for testing purposes.
Data Source
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AI summary
This disclosure relates to a system and method for at source data masking and discovery of unique identifier for at-source masking. The method reads a table of production database comprising sensitive column from a source database for at source data masking. A unique identifier column is identified, and a temporary table is created which has three or more columns. Columns of temporary table comprises a sensitive column from the table of production database, a column for masked data of sensitive column and a unique identifier column. Sensitive column of the temporary table is masked using a known masking technique and the original data of the sensitive column and the masked data of the sensitive column is inserted into the temporary table. Finally, the production database is updated with the masked data of the sensitive column.