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AI Research
arXiv CS.AI·Jun 26·5 min read
Agents That Know Too Much: A Data-Centric Survey of Privacy in LLM Agents
TL;DR
Large Language Models (LLMs) often handle sensitive data, raising privacy concerns. This survey reviews existing privacy-preserving techniques for LLMs, such as differential privacy and federated learning.
✦ Why It Matters
Engineers can leverage this survey to identify and implement better privacy-preserving techniques in LLM applications.
Key Takeaways
- Explore differential privacy to enhance data protection in LLMs.
- Consider federated learning for decentralized data processing.
- Assess the trade-offs between privacy and model performance.
- Stay updated on emerging privacy techniques for LLMs.
Full Summary
As Large Language Models (LLMs) become more prevalent, they frequently process sensitive user data, which poses significant privacy risks. This survey systematically examines various privacy-preserving techniques applicable to LLMs, including differential privacy, which adds noise to data to protect individual privacy, and federated learning, which allows models to learn from decentralized data without direct access.
The methodology involved a comprehensive review of existing literature and techniques, assessing their effectiveness in safeguarding user data. Key findings indicate that while some methods show promise, many lack robustness against sophisticated attacks or fail to balance privacy with model performance.
For instance, differential privacy can degrade model accuracy if not properly calibrated. These insights underscore the need for more effective privacy solutions tailored to the unique challenges posed by LLMs.