Real-Time Machine Learning Privacy Scoring for Unstructured Text
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Solution Overview
Problem
Existing content editing tools pose a risk of inadvertently disclosing sensitive information, which can be rapidly disseminated online, leading to privacy concerns due to the aggregation of seemingly innocuous data elements.
Innovation Solution
Implementing machine learning models to analyze unstructured text data in real-time, identify entities associated with privacy risks, and generate a privacy score based on entity connections, providing real-time feedback to users through graphical interfaces to modify content and reduce exposure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are used to analyze text data in real-time, then privacy risk detection capability is improved, but system complexity increases
Solution Approach 1:
The system divides the privacy risk detection process into separate functional modules: a natural language processing module for entity identification, a graph processing module for connection analysis, and a scoring module for risk evaluation. This segmentation allows each module to be optimized independently while working together to provide comprehensive privacy risk detection.
Solution Approach 2:
The patent introduces an intermediary graph data structure that mediates between the raw text input and the final privacy risk score. The graph represents entity relationships and serves as a bridge, allowing complex privacy assessments to be broken down into manageable steps through intermediate representations.
2Loss of time
If real-time analysis of unstructured text data is performed, then response time for privacy protection is improved, but processing speed requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-processing the text data to identify and extract entities before the main privacy risk assessment. This preliminary entity extraction creates a structured representation that can be quickly processed in subsequent steps, enabling real-time response while managing processing requirements.
Solution Approach 2:
The patent implements continuous processing where the system analyzes text data as it is entered or updated, maintaining continuous monitoring without requiring batch processing. This continuous action allows real-time privacy risk detection while optimizing performance through streaming processing techniques.
3Ease of operation
If automated machine learning analysis is applied to content, then manual subjective determinations are reduced, but automation extent increases
Solution Approach 1:
The system provides feedback to users by displaying the identified entities, their relationships, and associated privacy risk scores. This feedback mechanism allows users to understand the automated analysis results and make informed decisions about their content, reducing the need for manual subjective determinations while maintaining user control.
Solution Approach 2:
The patent implements self-service functionality where the system automatically analyzes content for privacy risks without requiring user intervention. The automated process independently identifies entities, evaluates risks, and provides recommendations, freeing users from manual analysis while maintaining ease of operation.
Data Source
AI summary
Systems and methods use machine learning models with content editing tools to prevent or mitigate inadvertent disclosure and dissemination of sensitive data. Entities associated with private information are identified by applying a trained machine learning model to a set of unstructured text data received via an input field of an interface. A privacy score is computed for the text data by identifying connections between the entities, the connections between the entities contributing to the privacy score according to a cumulative privacy risk, the privacy score indicating potential exposure of the private information. The interface is updated to include an indicator distinguishing a target portion of the set of unstructured text data within the input field from other portions of the set of unstructured text data within the input field, wherein a modification to the target portion changes the potential exposure of the private information indicated by the privacy score.