Dynamic Confidential Information Redaction via Token Database
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Solution Overview
Problem
Conventional technologies for preventing data leakage are often manual, time-consuming, and limited in application, failing to effectively address the dynamic nature of data exposure through unsecure communication channels, leading to potential legal liabilities and reputational damage.
Innovation Solution
A computer-implemented method using natural language processing and machine learning to create a classified token database, which dynamically identifies and redacts confidential information from communications by comparing tokens within the communications to the classified token database, enabling real-time protection against data leakage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual methods are used to prevent data leakage, then implementation is simple, but productivity is low and the system cannot adapt to dynamic data exposure
Solution Approach 1:
The patent implements dynamic redaction by continuously updating the classified token database with newly identified confidential information. The system adapts to changing data exposure patterns by learning from training input and adjusting its redaction capabilities in real-time, rather than relying on static manual rules
Solution Approach 2:
The system performs self-improvement by automatically training on identified confidential information and expanding its own classified token database without requiring constant manual intervention. The machine learning model continuously learns from new data patterns, enabling the system to serve itself in maintaining and improving its data protection capabilities
2Reliability
If comprehensive rediction of confidential information is implemented, then data protection is improved, but device complexity increases
Solution Approach 1:
The patent introduces a classified token database as an intermediary layer between the communication content and the redaction process. This database stores and manages classified information patterns, serving as a mediator that simplifies the comparison and rediction operations while maintaining comprehensive data protection capabilities
Solution Approach 2:
The system segments confidential information into discrete classified tokens that can be independently identified, stored, and applied for redaction. This segmentation allows the complex problem of comprehensive data protection to be broken down into manageable token-level operations, reducing overall system complexity
3Reliability
If real-time redaction is performed on communications, then data leakage prevention is improved, but loss of time in processing increases
Solution Approach 1:
The patent performs preliminary processing by training the machine learning model and building the classified token database in advance. This preliminary action prepares the system to quickly identify and redact confidential information in real-time communications without requiring complex analysis during the actual rediction process
Solution Approach 2:
The system uses token-based representations as simplified copies of confidential information patterns. Instead of analyzing entire communications for confidential content, the system compares token sequences against the classified token database, significantly reducing processing time while maintaining accurate identification of confidential information
Data Source
AI summary
A computer-implemented method for creating a classified token database usable for dynamically redacting confidential information from communications includes performing natural language processing on training input and determining whether a confidentiality level is present in the training input. The method includes, in response to determining that the confidentiality level is present, adding at least one classified token associated with the training input to a classified token database.