Automated Data Redaction via Corpus-Based Hashing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional data redaction techniques are often bespoke and inefficient for real-time processing of in-transit textual data, particularly in free-form dialog, leading to reduced accuracy and resource inefficiency due to reliance on specific catalogs of words.
Innovation Solution
A method for automated data redaction in real-time using a predefined corpus of linguistic components, involving monitoring, parsing, and modifying data streams to replace non-matching linguistic elements with hash values or tags, with the ability to update the corpus based on feedback and user input using machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional data redaction techniques are used, then specific catalogs of words can be redacted, but redaction accuracy is lowered for free-form dialog and resource efficiency is reduced
Solution Approach 1:
The system automatically updates the corpus of linguistic components by receiving feedback data from users and autonomously determining new linguistic components to add. This self-updating mechanism eliminates the need for manual corpus maintenance while improving redaction accuracy for free-form dialog over time, resolving the contradiction between accuracy and resource efficiency.
Solution Approach 2:
The system implements a feedback loop where user corrections and inputs are collected, processed, and used to automatically update the linguistic corpus. This feedback mechanism enables continuous improvement of redaction accuracy without requiring proportional increases in human resources, addressing the contradiction between precision and productivity.
2Adaptability or versatility
If bespoke redaction techniques with specific word catalogs are used, then implementation is straightforward, but adaptability to new linguistic components and phrases is poor
Solution Approach 1:
The system pre-processes user feedback and incoming data to identify potential new linguistic components before they are added to the corpus. This preliminary analysis prepares the system for future redaction scenarios, enabling adaptability without requiring complex real-time decision-making mechanisms.
Solution Approach 2:
The linguistic corpus is designed as a dynamic, evolving structure that automatically incorporates new linguistic components based on feedback. This dynamic nature allows the system to adapt to new phrases and terminology while maintaining a relatively simple underlying architecture, resolving the contradiction between adaptability and complexity.
3Reliability
If real-time processing of in-transit data is implemented, then data privacy compliance is improved, but processing speed and resource utilization are challenged
Solution Approach 1:
The system performs preliminary comparisons of linguistic elements against the corpus before final redaction decisions are made. This pre-filtering approach enables rapid processing of in-transit data while ensuring compliance, as most common words are quickly identified and handled without requiring full analytical processing.
Solution Approach 2:
The data stream is processed in segments or chunks rather than as a monolithic flow. This segmentation allows parallel processing of multiple linguistic elements simultaneously, maintaining real-time compliance capabilities while improving overall processing throughput and resource utilization.
Data Source
AI summary
A method for facilitating automated data redaction in real-time based on a corpus of linguistic components is disclosed. The method includes monitoring, in real-time, a data stream between a variety of applications; detecting textual data in the data stream, the textual data including free-form dialog data; parsing the textual data to identify linguistic elements, the linguistic elements including words and phrases; determining whether redaction is required for each of the linguistic elements; comparing each of the linguistic elements with the corpus when redaction is not required; and modifying, in real-time, the data stream to replace the linguistic elements with hash values when the linguistic elements do not match the linguistic components in the corpus.