Contextual Data Obfuscation for Secure Raw Data Removal
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Solution Overview
Problem
Existing big data systems face inefficiencies and security risks due to the storage of large datasets, which can lead to unnecessary data collection, exposure of sensitive information, and inconsistent interpretation, while maintaining raw data introduces privacy and processing challenges.
Innovation Solution
A method involving irreversible encryption and multi-dimensional obfuscation of data, converting textual data into visual representations and blending contextually similar elements to generate aggregate results, while discarding original datasets, ensuring contextual meaning is retained without revealing precise data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If large datasets are stored and collected, then data analysis capability is improved, but security risks and storage costs increase
Solution Approach 1:
The patent segments data into two distinct components: contextual information (preserved) and raw data (removed). This segmentation allows the system to maintain analytical capability through contextual maps while eliminating security risks associated with storing raw sensitive data. The contextual map contains only the essential meaning and relationships needed for analysis, discarding identifiable personal information.
Solution Approach 2:
The patent extracts and removes raw data after its contextual meaning has been captured. The system takes out only the necessary contextual information from the raw dataset, preserving it in a simplified format while discarding the rest. This extraction process maintains analytical utility while eliminating security vulnerabilities.
2Measurement precision
If more data is collected, then analytical accuracy is improved, but storage costs and processing effort increase
Solution Approach 1:
The patent extracts only the essential contextual meaning from raw data, creating a condensed representation that maintains analytical accuracy. By taking out only the necessary contextual information and discarding redundant raw data, the system preserves measurement precision while dramatically reducing processing effort and storage requirements.
Solution Approach 2:
Instead of storing raw data and processing it to extract meaning, the patent inverts the approach by directly extracting and storing only the contextual meaning. This inversion eliminates the need to process and store vast amounts of raw data, reducing processing effort while maintaining analytical accuracy.
3Loss of information
If raw data is stored with origin tracking, then data provenance is maintained, but privacy protection is compromised
Solution Approach 1:
The patent segments data into contextual information (preserved) and raw data (removed). This segmentation allows the system to maintain data provenance through contextual maps that capture the meaning and origin of information while eliminating privacy exposure by discarding identifiable personal data.
Solution Approach 2:
The patent creates a contextual copy or representation of the raw data that preserves the essential meaning and provenance information without containing the actual sensitive data. This copying approach maintains data origin tracking while protecting privacy, as the contextual map is a simplified representation that cannot be used to identify individuals.
4Adaptability or versatility
If contextual meaning is preserved through mapping, then data utility is maintained, but data security is improved
Solution Approach 1:
The patent segments data into contextual information (preserved) and raw data (removed). This segmentation maintains data utility through contextual maps that preserve meaning and relationships while improving data security by eliminating sensitive raw data.
Solution Approach 2:
The patent extracts and preserves only the contextual meaning from raw data, maintaining data utility while improving security. By taking out the essential contextual information and discarding the rest, the system achieves both goals simultaneously.
Data Source
AI summary
Ingesting large quantities of data in a secure manner can be problematic, particularly processing types of data streams to determine the content of the data stream. As provided herein, a context associated with the data stream can be ascertained by mapping the content of data stream using contextual maps. The content and context can then be further processed in order to generate appropriate responses. In addition, obfuscation can be applied to the content such that the original content is lost while the contextual meaning associated with the content is maintained. In this way, an understanding can persist of the original content without retaining the underlying raw data.