Phonetic Data Masking System for Enterprise Integrity
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Solution Overview
Problem
Existing data masking solutions fail to effectively maintain data integrity and consistency by not considering phonetically similar variants as a single group, restrict data masking to specific file types, and allow backward traceability of original data, limiting their applicability across various data sources.
Innovation Solution
A computer-implemented method and system that preprocesses input dataset values to determine phonetically similar groups, generates numeric codes for selecting masked values from fictitious data groups, and extends data masking to diverse data sources like RDBMS databases and documents, eliminating the need for pre-mapped metaphone lists.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If phonetically similar variants are not grouped together during masking, then data privacy is improved through more varied masking, but data integrity and consistency deteriorate across enterprise systems
Solution Approach 1:
The patent merges phonetically similar data variants into unified masking groups using phonetic algorithms (Soundex, Metaphone). This combines multiple similar variants into single groups, ensuring consistent masking across enterprise systems while maintaining data privacy. The grouping mechanism unifies the treatment of phonetically equivalent data without requiring manual configuration.
Solution Approach 2:
The system performs self-service by automatically generating phonetic groups and assigning masking rules without requiring manual intervention. The phonetic algorithms automatically categorize data variants, and the system self-configures masking strategies based on detected phonetic patterns, reducing operational complexity while ensuring consistency.
2Adaptability or versatility
If metaphone mapping lists are pre-maintained, then masking accuracy for known data is improved, but system adaptability to new data deteriorates due to manual update requirements
Solution Approach 1:
The patent implements dynamic metaphone list generation that adapts to new data automatically. Instead of static pre-maintained lists, the system dynamically creates and updates phonetic groupings based on encountered data, ensuring continuous adaptability without manual intervention. This dynamic approach allows the system to handle new data types and variants as they appear.
Solution Approach 2:
The system performs preliminary phonetic analysis and grouping during the initial data processing phase, preparing masking groups in advance for future masking operations. This preliminary action ensures that when new data arrives, the phonetic grouping mechanism is already in place and can immediately process it without requiring manual list updates.
3Reliability
If masking replaces all variants with a single masked value, then data privacy is improved, but data variation and distribution characteristics deteriorate
Solution Approach 1:
The patent applies local quality by differentiating masking strategies based on phonetic group characteristics. Instead of uniform masking across all data, the system applies context-aware masking that preserves variation within phonetically distinct groups while maintaining privacy. Each phonetic group receives appropriate masking treatment that considers its specific characteristics and distribution patterns.
Solution Approach 2:
The system changes masking parameters based on phonetic group properties and data distribution characteristics. By adjusting masking intensity, substitution methods, and group selection based on phonetic analysis results, the system maintains both privacy protection and data variation. The masking parameters are dynamically modified to preserve essential data characteristics while protecting sensitive information.
4Adaptability or versatility
If data masking is restricted to file types, then implementation simplicity is improved, but system versatility deteriorates across different data sources
Solution Approach 1:
The patent implements universality by designing a phonetic-based masking system that operates across multiple data sources and formats (databases, files, streams). The core phonetic grouping mechanism is format-agnostic, allowing the same system to handle diverse data sources without requiring source-specific implementations, thus achieving multi-functionality while controlling complexity.
Data Source
AI summary
A method and system is provided for generating a group of phonetically similar masked data. The present application provides a method and system for generating a group of phonetically similar masked data; comprises preprocessing of input dataset values comprising a list of fictitious data values to be used as masked data; determining a plurality of groups of phonetically similar data values present in the dataset list; and deriving metaphone for each input data value to be masked; generating a first numeric code from derived metaphone value of input data value to be masked; selecting one group of phonetically similar data values out of the plurality of groups of phonetically similar data values based on the generated first numeric code; and generating a second numeric code from input data value for selecting a masked value from a plurality of fictitious data group.