Sensitive Text Detector Generation Using Regex and Keyword Extraction
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Solution Overview
Problem
Existing sensitive text detectors require manual customization of rules, which is time-consuming and accuracy-dependent on user expertise, and struggle to maintain consistent accuracy across varying text inputs.
Innovation Solution
A method for generating sensitive text detectors using a heuristic algorithm to create regular expressions and unsupervised keyword extraction, trained on a set of text inputs, to automatically customize the detector for improved accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual customization of sensitive text detector rules is used, then the detector can be tailored to specific needs, but the process is time-consuming and requires user expertise
Solution Approach 1:
The system automatically generates sensitive text detector rules by analyzing training data and extracting patterns, eliminating the need for manual rule creation. The algorithm self-configures the detector parameters based on the provided text samples, making the system self-sufficient in the customization process.
Solution Approach 2:
The patent replaces the manual mechanical process of rule-writing with an automated computational algorithm. The system uses machine learning techniques to automatically generate regular expressions and detection rules from training data, substituting human expertise with an automated computational system.
2Adaptability or versatility
If hand-written rules are used for sensitive text detection, then the detector can be customized, but the accuracy depends on user expertise
Solution Approach 1:
The system incorporates a feedback mechanism where the algorithm evaluates generated rules against training data and iteratively improves detection accuracy. The system learns from positive and negative examples, adjusting its rule generation process to maximize precision while maintaining adaptability.
Solution Approach 2:
The patent performs preliminary analysis of training data to identify patterns and characteristics of sensitive text before generating detection rules. This preliminary action allows the system to pre-configure optimal detection parameters based on the specific characteristics of the target data, improving accuracy without requiring manual tuning.
3Reliability
If existing sensitive text detectors are used, then detection can be performed, but they struggle to maintain consistent accuracy across varying text inputs
Solution Approach 1:
The system dynamically adapts its detection parameters and rules based on the characteristics of the input text. Rather than using fixed rules, the algorithm adjusts its detection strategy in real-time based on the patterns learned from training data, maintaining consistent accuracy across diverse text types.
Solution Approach 2:
The patent creates a universal detector framework that can handle multiple text types and formats through a single adaptive system. The generated rules are designed to be broadly applicable while maintaining precision, allowing the detector to consistently perform across various text inputs without requiring separate customization for each text type.
Data Source
AI summary
The present disclosure relates to one or more processors, communicative with one or more computer-readable media, are configured to automatically generate a sensitive text detector including a regular expression or keyword. A set of text inputs, including sensitive text, are received. The sensitive text is extracted from the set of text inputs. Based on the extracted sensitive text, one or both of the regular expression and the keyword are generated. The generated regular expression and/or keyword are used to generate a sensitive text detector for sensitive text detection.