Privacy Engine for Automated Document Redaction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional systems for managing sensitive information under privacy laws and regulations are inefficient, leading to high administrative and financial costs due to the complexity of compliance and accuracy in identifying privacy information.
Innovation Solution
The Privacy Engine employs machine learning models and Natural Language Processing techniques to categorize and redact sensitive information within electronic documents, providing a customizable solution for accurate detection and compliance reporting, while allowing human review and feedback to improve model accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems are used for sorting content with sensitive information, then basic privacy protection is provided, but administrative and financial costs are high and accuracy in identifying privacy information is insufficient
Solution Approach 1:
The patent replaces manual review and conventional sorting systems with machine learning models and natural language processing techniques. The system automatically detects, categorizes, and redacts privacy information using AI algorithms, eliminating the need for human administrators to manually review documents while significantly improving detection accuracy.
Solution Approach 2:
The machine learning system performs self-training and self-improvement through automated feedback loops. The system automatically learns from detected privacy information patterns, adjusts its detection algorithms, and refines its categorization capabilities without requiring external intervention, thereby reducing administrative overhead while maintaining high accuracy.
2Productivity
If manual review and conventional sorting methods are used, then human oversight is possible, but productivity is low and time consumption is high
Solution Approach 1:
The patent replaces manual review processes with automated machine learning systems that can process vast volumes of documents simultaneously. The system performs detection, categorization, and redaction operations in parallel, reducing processing time from days or weeks to minutes or seconds while maintaining consistent accuracy across all documents.
Solution Approach 2:
The system performs preliminary detection and categorization of privacy information before final redaction decisions are made. By pre-identifying and classifying sensitive content using machine learning models, the system prepares documents for rapid redaction and compliance reporting, significantly reducing the time required for the complete compliance workflow.
3Adaptability or versatility
If conventional redaction systems are used, then basic compliance reporting is achieved, but customization and adaptability to different privacy regulations are limited
Solution Approach 1:
The patent implements a dynamic machine learning system that can adapt its detection parameters, categorization rules, and redaction strategies based on different privacy regulations. The system automatically adjusts its behavior according to the specific compliance requirements of different jurisdictions, allowing organizations to handle multiple regulatory frameworks with a single configurable platform.
Solution Approach 2:
The machine learning platform provides universal functionality for detecting and redacting various types of privacy information across different document formats and regulatory contexts. A single system can handle personally identifiable information, health data, financial records, and other sensitive content types, adapting its detection algorithms to meet diverse compliance requirements without requiring separate specialized systems.
Data Source
AI summary
Methods, systems and computer-program products are directed to a Privacy Engine for evaluating initial electronic documents to identify document content categories for portions of content within the electronic documents, with respect to extracted document structures and document positions, that may include privacy information for possible redaction via visual modification. The Privacy Engine builds a content profile based on detecting information at respective portions of electronic document content that indicate one or more pre-defined categories and/or sub-categories. For each respective portion of electronic document content, the Privacy Engine applies a machine learning model that corresponds with the indicated category (or categories and sub-categories) to determine a probability value of whether the respective portion of content includes data considered likely to be privacy information. The Privacy Engine recreates the one or more initial electronic documents according to one or more privacy information redactions at respective locations of the portions of content.