Content-Specific Structure for Multi-Format Data Masking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data masking technologies fail to effectively handle multiple formats of data with different types while masking sensitive information, as they do not provide a methodology to identify and isolate sensitive data across various data structures and formats.
Innovation Solution
A method and system that create a content-specific structure (CSS) and a generalized masking structure (GMS) to organize and mask input content, allowing for the identification and protection of sensitive data across different data types by parsing the content into a structured format and associating sensitivity attributes with each node, independent of the underlying data representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional data masking methods are used, then simple data types can be masked, but multiple formats of data with different data types cannot be effectively handled
Solution Approach 1:
The patent creates a universal masking system that can handle multiple data formats (XML, JSON, HTML, CSV, etc.) through a common architecture. The Content Specific Structure (CSS) and Generalized Masking Structure (GMS) serve as format-agnostic intermediaries that enable the same masking engine to process diverse data types without requiring format-specific masking logic, thus achieving multi-functionality while maintaining system coherence.
Solution Approach 2:
The patent introduces intermediate structures (CSS and GMS) that act as mediators between the raw input content and the masking process. These intermediate structures normalize different data formats into a common representation, allowing the masking system to operate on a standardized form regardless of the original format, thereby resolving the contradiction between handling multiple formats and maintaining system simplicity.
2Reliability
If sensitive data is masked across various data structures, then data protection is improved, but the ability to identify and isolate sensitive data becomes more difficult
Solution Approach 1:
The patent segments the masking process into distinct phases: parsing input content into CSS nodes, analyzing each node against sensitivity policies, creating GMS nodes with associated sensitivity attributes, and finally applying masking. This segmentation allows sensitive data to be systematically identified and isolated at the node level, making the detection and measurement of sensitive data tractable while ensuring comprehensive protection.
Solution Approach 2:
The patent changes the parameter representation of data by transforming raw content into structured nodes with explicit sensitivity attributes. Each CSS node and corresponding GMS node carries metadata about sensitivity levels, data types, and policy compliance status. This parameter transformation makes sensitive data easily detectable through attribute inspection while maintaining strong protection through policy-based masking rules.
3Manufacturing precision
If a generalized masking structure is created for each content type, then masking accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by creating the Content Specific Structure (CSS) and Generalized Masking Structure (GMS) before applying masking rules. The CSS parsing and GMS node creation establish a standardized framework that pre-organizes the data and identifies sensitive elements. This preliminary structuring enables more accurate masking by ensuring all sensitive data is properly identified and tagged before the actual masking operation, while the modular nature of the approach allows for efficient processing.
Data Source
Figure 1
Figure 2
Figure 3(a)
AI summary
Systems and methods for masking content of different types are described. The system may implement the method comprising receiving a request to mask input content that includes sensitive and non-sensitive data. The method also comprises parsing the input content to create a content specific structure (CSS) to organize the input content in a structured format. The CSS includes a plurality of CSS nodes. The method analyses each CSS node against a pre-defined privacy policy and identifies sensitivity of the each CSS node. The method also creates a generalized masking structure (GMS) by creating a GMS node corresponding to each CSS node. Each GMS node contains sensitivity information related to corresponding CSS node. Data represented by GMS is masked based upon the sensitivity information stored in it.