Automatic Extraction of Protected Health Information from Multilingual Hacker Communities

Abstract

The protection of health information is critical in cybersecurity, particularly as healthcare data becomes increasingly valuable to malicious actors. This paper presents a novel approach for automatically extracting protected health information (PHI) from multilingual hacker communities using advanced machine learning techniques.

Our research addresses the challenges of:

• Multilingual Processing: Handling PHI across different languages and scripts
• Context Awareness: Understanding the context in which health information appears
• Privacy Protection: Ensuring compliance with healthcare privacy regulations
• Real-time Detection: Providing timely identification of PHI exposure

The framework demonstrates high accuracy in identifying PHI across multiple languages while maintaining low false positive rates.

Key Contributions

1. Multilingual Framework: Development of language-agnostic PHI detection
2. Context-Aware Analysis: Understanding PHI within broader communication contexts
3. Privacy Compliance: Ensuring adherence to healthcare privacy standards
4. Real-time Capabilities: Providing immediate PHI detection and alerting

Research Impact

This work contributes to the protection of healthcare data in cyberspace and provides tools for organizations to monitor and protect sensitive health information.