Pattern Recognition for Sensitive Data Detection
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Solution Overview
Problem
Existing data security techniques struggle to efficiently identify sensitive information in large volumes of data, as manual detection is infeasible and traditional rules-based systems can overlook patterns, especially in big data scenarios with high velocity and variety.
Innovation Solution
The system employs machine learning techniques to automatically detect sensitive information by training feature functions and utilizing dynamically generated feature functions to identify discriminants, reducing processing time and system resource costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual detection methods are used to identify sensitive information, then detection accuracy can be maintained at a reasonable level, but the processing time and resource costs become prohibitively high for large volumes of data
Solution Approach 1:
The system dynamically generates feature functions based on learned patterns from training data, allowing the detection mechanism to adapt and evolve. The feature functions are not static but are continuously refined through machine learning, enabling the system to maintain high detection accuracy while automatically processing large volumes of data without manual intervention
Solution Approach 2:
The system performs self-training by automatically learning patterns from labeled sensitive information and generating its own feature functions without requiring continuous manual configuration. The machine learning model autonomously improves detection capabilities by processing training data and generating optimized detection rules, reducing both time and resource requirements for ongoing operations
2Productivity
If traditional rules-based systems are used to detect sensitive information, then implementation is straightforward and fast, but the systems cannot adapt to new patterns and overlook emerging sensitive information types
Solution Approach 1:
The system performs preliminary learning by training on labeled sensitive information data before actual detection operations. This pre-training phase generates optimized feature functions that capture patterns in sensitive information, enabling the system to maintain high detection efficiency while being prepared to recognize both known and emerging patterns without requiring real-time adaptation during detection
Solution Approach 2:
The system changes its detection parameters dynamically by generating and updating feature functions based on learned patterns. Instead of using fixed rules, the machine learning model adjusts its internal parameters and detection criteria based on training data, enabling adaptability to new patterns while maintaining efficient automated detection operations
3Ease of operation
If static rules are used for sensitive information detection, then system complexity is reduced and ease of operation is improved, but detection accuracy decreases when dealing with diverse and evolving data patterns
Solution Approach 1:
The system automatically learns and generates detection rules from training data without requiring manual configuration or intervention. The machine learning model self-adjusts its feature functions based on patterns in labeled sensitive information, maintaining ease of operation while significantly improving detection accuracy for diverse and evolving data patterns
Solution Approach 2:
The system dynamically adjusts detection parameters by generating optimized feature functions from training data. This automatic parameter optimization enables the system to maintain simple operation interfaces while achieving high detection accuracy through learned patterns, resolving the trade-off between ease of operation and detection precision
Data Source
AI summary
Methods and systems for identifying sensitive information are provided. The method includes tokenizing labeled data into first word sequences, the labeled data including sensitive information. The method includes associating the labeled sensitive information with tags. The method includes determining that the first word sequences and the tags satisfy conditions defined by feature functions. The method includes calculating a local maximum of a likelihood function to determine a weight. The method includes tokenizing unlabeled data into second word sequences, the unlabeled data including sensitive information. The method includes executing each feature function based on their weights, the second word sequences, and tag sequences. The method includes selecting tag sequences that maximize probabilities of the second word sequences based on the likelihood function. The method includes identifying sensitive information in the unlabeled data based on the selected tag sequences.