Document Masking Using Relational Models to Block Data Reconstruction
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Solution Overview
Problem
Current data protection methods fail to consider indirect information sources that can be used to reconstruct masked sensitive information, leaving documents vulnerable to data leaks.
Innovation Solution
A computer-implemented method that identifies and masks sensitive data by analyzing a relational model of interrelated datasets, using classification-based masking and indexing techniques to protect sensitive information beyond what is directly contained in the document.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current parsing and masking methods are used to protect sensitive information, then direct sensitive data in documents is masked, but indirect information sources that can reconstruct sensitive data are not considered
Solution Approach 1:
The patent extends data protection from a single-document dimension to a multi-dimensional approach by integrating relational database contexts. It queries related datasets (tables, columns, rows) that connect to the document's entities, protecting sensitive information across multiple data dimensions including direct document content and indirect relational data sources.
Solution Approach 2:
The patent introduces an intermediary layer between document parsing and masking by incorporating a relational model query system. This intermediary queries database tables, columns, and rows to identify additional sensitive attributes related to document entities, thereby expanding protection beyond the document's explicit content to include connected data sources.
2Productivity
If only document-contained information is considered for masking, then processing is simple and fast, but data protection is insufficient against reconstruction attacks
Solution Approach 1:
The patent performs preliminary actions by pre-establishing a relational model that maps relationships between documents and database entities (tables, columns, rows). Before masking occurs, the system pre-queries related datasets to identify all potentially sensitive attributes, enabling comprehensive protection without significantly increasing processing time during actual document handling.
3Reliability
If comprehensive relational model querying is performed to identify all sensitive attributes, then data protection is enhanced, but system complexity and query time increase
Solution Approach 1:
The patent segments the data protection process into distinct modular components: document parsing, entity extraction, relational model querying (with separate handling of tables, columns, rows), and masking. This segmentation allows each component to be optimized independently and facilitates maintenance while achieving comprehensive sensitive attribute identification across the entire data ecosystem.
Data Source
AI summary
In an approach, a processor receives a request of a document. A processor identifies a set of datasets comprising a sensitive dataset, the set of datasets being interrelated in accordance with a relational model. A processor extracts attribute values of the document. A processor determines that a set of one or more attribute values of the extracted attribute values is in the set of datasets, the set of attribute values being values of a set of attributes. A processor determines that one or more entities of the sensitive dataset can be identified based on relations of the relational model between the set of attributes, where at least part of attribute values of the one or more entities comprises sensitive information. A processor, responsive to determining that the one or more entities can be identified, masks at least part of the set of one or more attribute values in the document.