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Privacy-Preserving Mechanisms for LLMs by Bakary Dolo is a document available to read on EtoBox.

What is Privacy-Preserving Mechanisms for LLMs about?

This document surveys privacy-preserving mechanisms for large language models (LLMs), addressing privacy challenges such as data leakage and adversarial attacks. It evaluates techniques like differential privacy, federated learning, and cryptographic methods, while discussing their applications in sensitive domains like healthcare and finance. The paper emphasizes the need for integrating privacy by design into LLM development to ensure data confidentiality without compromising performance.

Author

Bakary Dolo

Language

EN