Cross-Border Data Pipeline With Local PII Isolation
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Solution Overview
Problem
Existing data systems struggle with maintaining scalability, stability, and complexity, and often fail to protect personally identifying information (PII) when transmitting data across countries.
Innovation Solution
A multi-country data pipeline that utilizes a validated data pipeline architecture, ensuring PII remains within its country of origin by identifying and anonymizing data using a schema service and ID tags, while allowing non-PII data to be analyzed and transmitted across borders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If personally identifying information (PII) is retained in data lakes for analytics and machine learning, then data utility and model accuracy are improved, but security risks and compliance violations increase
Solution Approach 1:
The system extracts PII from data lakes using automated discovery tools that scan and identify personally identifiable information. Once identified, the PII is separated from the bulk data through masking or removal operations, allowing the remaining anonymized data to be used for analytics while eliminating security risks associated with retaining actual PII.
Solution Approach 2:
The patent introduces an intermediary layer between data storage and data usage that includes automated PII discovery, classification, and masking capabilities. This intermediary system allows data to flow freely for analytics purposes while automatically protecting PII through dynamic masking rules, thus resolving the contradiction between data utility and security.
2Adaptability or versatility
If data is shared across multiple countries and organizations, then collaboration and analytics capability are improved, but data governance and compliance complexity increase
Solution Approach 1:
The system implements a universal data governance framework that automatically applies across multiple countries and organizations. The framework includes standardized PII discovery mechanisms, classification taxonomies, and masking rules that work consistently across different jurisdictions, enabling collaboration while simplifying governance through automation rather than manual processes.
Solution Approach 2:
The patent employs dynamic parameter changes in data masking based on jurisdiction, data type, and usage context. Automated systems adjust masking parameters (such as masking level, replacement values, or anonymization techniques) according to local regulations and data sensitivity, enabling flexible cross-border data sharing without requiring complex manual governance configurations for each scenario.
3Manufacturing precision
If manual PII identification and redaction processes are used, then compliance accuracy may be improved, but processing time and operational costs increase
Solution Approach 1:
The system replaces manual mechanical processes of PII identification and redaction with automated computational systems. Machine learning models and natural language processing algorithms automatically discover, classify, and mask PII throughout data lakes, achieving both high accuracy through sophisticated detection algorithms and high speed through automated processing, eliminating the trade-off between manual accuracy and automated speed.
Solution Approach 2:
The patent implements self-service automated PII protection systems that continuously monitor and protect data without requiring manual intervention. The system automatically discovers new PII, updates masking rules based on changing regulations, and maintains compliance autonomously, achieving both accuracy through intelligent algorithms and productivity through elimination of manual processes.
Data Source
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AI summary
A multi-country data pipeline keeps all of the Pll received from a user that is in a first country in the first country. The data pipeline allows the non-personal data received from the user to be transmitted and analyzed in a second country. The method further allows the results of the analysis in the second country to be transmitted back to the first country where the Pll is added to the results of the analysis. The data pipeline allows the results of the analysis in the second country to be used to take a desired action for the user in the first country, all while the Pll of the user never leaves the first country.