Safeguarding Privacy in AI Language Models
Novel approaches to protect personal data in LLM applications
This research addresses critical privacy vulnerabilities in Large Language Models that can expose sensitive user information across healthcare, finance, and customer service.
- Identifies risks of PII exposure through unintentional memorization in LLMs
- Proposes privacy preservation techniques to prevent data extraction attacks
- Establishes frameworks for secure LLM deployment in sensitive industry applications
- Balances privacy protection with maintaining model utility
This work is crucial for security professionals as it provides actionable strategies to mitigate privacy risks when implementing generative AI solutions, helping organizations comply with data protection regulations while leveraging LLM capabilities.