Neural Network PII Identification in Data Integration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for data integration processes lack an efficient system to accurately identify personal identifiable information (PII) and apply appropriate security measures during data migration, which is crucial for compliance with regulations like GDPR.
Innovation Solution
A PII recommendation system that uses a feed-forward neural network to analyze metadata properties and suggest labeling of field values as containing PII data, based on comparison with previously migrated PII field values, and recommends appropriate security measures such as masking functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification of PII data is used, then accuracy and control are improved, but time consumption and labor requirements increase
Solution Approach 1:
The system enables self-service by automatically identifying PII data through neural network analysis of metadata properties. The system analyzes field value characteristics, data types, and patterns to autonomously determine PII presence, eliminating the need for manual review while maintaining high accuracy through machine learning models trained on PII recognition tasks.
Solution Approach 2:
The patent replaces manual mechanical identification processes with an automated neural network-based system. The neural network processes metadata properties and field value patterns to automatically detect PII, substituting human cognitive processing with computational algorithms that achieve consistent and scalable PII identification.
2Productivity
If automated PII identification systems are implemented, then processing speed is improved, but system complexity and false identification risks increase
Solution Approach 1:
The system segments the PII identification task into distinct analytical components: analyzing metadata properties, evaluating field value patterns, and classifying data types. This segmentation allows the neural network to process complex data systematically through multiple independent analysis layers, improving both speed and accuracy while managing complexity through modular architecture.
Solution Approach 2:
The system changes parameters by analyzing multiple metadata properties simultaneously (data type, format, length, pattern characteristics) and adjusting the neural network's analysis depth based on data complexity. This parameter-based approach enables the system to process simple data quickly while applying more thorough analysis to complex datasets, optimizing the balance between speed and accuracy.
3Reliability
If security measures are applied to all data, then data protection is improved, but data availability and usability decrease
Solution Approach 1:
The system applies local quality by selectively applying security measures only to identified PII fields rather than uniformly to all data. The neural network identifies which specific field values contain PII, allowing the system to apply masking, encryption, or other security measures only to those specific locations, thereby maintaining security where needed while preserving accessibility where data is not sensitive.
Solution Approach 2:
The system uses feedback mechanisms where the neural network continuously refines its PII identification based on analyzed metadata properties and field value patterns. This feedback loop allows the system to learn from previous identifications and improve its accuracy, ensuring that security measures are applied precisely to actual PII while minimizing unnecessary restrictions on non-sensitive data.
Data Source
AI summary
An information handling system operating a personal identifiable information (PII) recommendation system may comprise a GUI modelling with visual integration elements, an integration process flow for migrating field values comprising PII data, wherein the integration process applies a security measure to migration of PII data, a processor executing code instructions to generate a migrating field value term frequency vector describing a weighted frequency with which a metadata term for the migrating field value appears within a metadata for a migrating dataset comprising the migrating field value, input the term frequency vector into a trained neural network to determine the migrating field value includes PII data, label the migrating field value as PII data, such that the modeled integration process applies the security measure to the migrating field value, and a network interface device transmitting a set of connector code instructions for performing the modeled integration process for remote execution.