Data Change Repository for Sensitive Data Validation
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Solution Overview
Problem
The transmission of bulk data between storage locations is resource-intensive and exposes data to loss, corruption, and security risks, while shared data repositories face challenges in maintaining data consistency and accuracy across multiple entities.
Innovation Solution
The system maintains a shared data repository by using a data change repository that stores data labels for each record, including a stateless value and a modification characteristic, to identify and validate data without affecting security protocols or access restrictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If bulk data transmission is used to share data between storage locations, then data accessibility is improved, but resource consumption increases and security risks worsen
Solution Approach 1:
The patent uses data labels (metadata) to represent and track data characteristics instead of transmitting actual data copies. Each storage location maintains labels that describe the data (format, sensitivity, validation rules) without needing to duplicate the underlying data, enabling shared access patterns while keeping data locally stored and resources efficient.
Solution Approach 2:
The patent introduces data labels as an intermediary layer between storage locations. These labels act as mediators that carry information about data characteristics, validation requirements, and security protocols without requiring direct data transmission. The labels enable coordination and validation across distributed systems while minimizing actual data movement.
2Loss of time
If multiple entities independently update shared data, then data freshness is improved, but data consistency worsens
Solution Approach 1:
The patent implements feedback mechanisms where data labels are validated against stored validation rules after each update. When an entity updates shared data, the system automatically validates the update using criteria defined in the data labels (format requirements, sensitivity classifications, business rules). This feedback loop ensures that only valid updates are accepted, maintaining consistency while allowing concurrent updates from multiple entities.
Solution Approach 2:
The patent applies preliminary action by pre-defining validation rules and data characteristics in the data labels before updates occur. These labels contain predetermined criteria (data formats, required fields, sensitivity levels) that guide and constrain how data can be updated. By establishing these rules in advance, the system prevents inconsistent updates before they can corrupt data integrity.
3Reliability
If data security protocols and access restrictions are enforced, then data security is improved, but data validation capability worsens
Solution Approach 1:
The patent segments data protection into two independent layers: data labels (metadata) and underlying data. Validation operations are performed on the labels, which contain information about data characteristics and requirements, without exposing or accessing the sensitive underlying data. This segmentation allows validation to proceed while security protocols remain intact, as the system validates descriptions rather than the actual sensitive information.
Solution Approach 2:
The patent uses copies of data characteristics stored in labels to perform validation. Instead of accessing the actual sensitive data to validate it, the system validates copies of the data's metadata (format requirements, expected structures, sensitivity classifications). This approach enables comprehensive validation while maintaining security, as the validation process operates on non-sensitive label data rather than the protected underlying data.
Data Source
AI summary
Systems and methods utilize a data change repository that supplements existing data in a database by storing various data labels for each record. Each data label comprises a first data characteristic comprising a stateless value and a second data characteristic that comprises a modification characteristic of the stateless value. The systems and methods may then use these additional labels to identify and/or validate the underlying data. As the labels are used for the identification and/or validation (as opposed to the underlying data itself), the systems and methods do not affect, and function despite, security protocols, access restrictions, and/or viewing rights.