Automated PII Detection in Click Stream Data
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Solution Overview
Problem
Conventional methods for removing personally identifying information (PII) from data streams, such as click streams, are inadequate as they rely on explicit matching or manual rule creation, which are inefficient and fail to effectively identify and obscure private information in an ever-changing web environment.
Innovation Solution
The system automatically analyzes click stream data to identify explicit, implicit, and derived private information using statistical analysis and filtering methods, allowing for the detection and removal or obscuration of PII without significant human intervention, adapting to new and evolving web environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If explicit match or manual rule creation is used to remove PII, then the process is simple to implement, but the effectiveness and coverage of PII detection is insufficient
Solution Approach 1:
The system automatically generates removal rules by analyzing data stream patterns and identifying PII types without requiring manual rule creation. The algorithm self-adapts to new PII formats by learning from data characteristics, eliminating the need for continuous manual tuning while maintaining high detection effectiveness.
Solution Approach 2:
The system dynamically adjusts detection parameters such as pattern matching sensitivity, rule priority weights, and identification thresholds based on the specific data stream being analyzed. This allows the same system to effectively handle different types of data (URLs, logs, databases) by automatically optimizing parameters for each context.
2Productivity
If manual rule creation is used, then initial PII removal can be achieved, but substantial time and resources are required to maintain and update rules
Solution Approach 1:
The system automatically monitors data streams for new PII patterns and formats, generates updated removal rules, and deploys them without human intervention. This self-updating capability eliminates the continuous manual maintenance effort while keeping the system current with emerging PII types.
Solution Approach 2:
The system incorporates feedback loops where detected PII instances and removal outcomes are analyzed to continuously improve rule effectiveness. The system learns from false positives and negatives, automatically refining its detection algorithms and rule sets to maintain high productivity over time.
3Reliability
If explicit match method is used, then implementation is straightforward, but only a small fraction of private information is removed
Solution Approach 1:
The system segments PII detection into multiple hierarchical layers: explicit pattern matching, contextual analysis, statistical anomaly detection, and relationship-based inference. Each layer handles different types of PII with appropriate complexity, collectively achieving comprehensive removal while managing system complexity through modular architecture.
Solution Approach 2:
The system introduces intermediary analysis components that bridge simple pattern matching and complex AI-based detection. These intermediaries include statistical analysis modules, contextual understanding layers, and pattern recognition algorithms that progressively refine detection accuracy without requiring the entire system to be maximally complex.
Data Source
AI summary
A method and apparatus for an automated classification of data in a data stream as private data includes receiving a data stream, parsing the data stream to determine parameter labels and parameter values; statistically analyzing the parameter labels and parameter value, and determining parameter labels corresponding to private data based on the analysis.