With the rise of large language models (LLMs), increasing research has recognized their risk of leaking personally identifiable information (PII) under malicious attacks. Although efforts have been made to protect PII in LLMs, existing methods struggle to balance privacy protection with maintaining model utility. In this paper, inspired by studies of amnesia in cognitive science, we propose a novel approach, Proactive Privacy Amnesia (PPA), to safeguard PII in LLMs while preserving their utility. This mechanism works by actively identifying and forgetting key memories most closely associated with PII in sequences, followed by a memory implanting using suitable substitute memories to maintain the LLM's functionality. We conduct evaluations across multiple models to protect common PII, such as phone numbers and physical addresses, against prevalent PII-targeted attacks, demonstrating the superiority of our method compared with other existing defensive techniques. The results show that our PPA method completely eliminates the risk of phone number exposure by 100% and significantly reduces the risk of physical address exposure by 9.8% - 87.6%, all while maintaining comparable model utility performance.
View on arXiv@article{kuo2025_2502.17591,
title={ Proactive Privacy Amnesia for Large Language Models: Safeguarding PII with Negligible Impact on Model Utility },
author={ Martin Kuo and Jingyang Zhang and Jianyi Zhang and Minxue Tang and Louis DiValentin and Aolin Ding and Jingwei Sun and William Chen and Amin Hass and Tianlong Chen and Yiran Chen and Hai Li },
journal={arXiv preprint arXiv:2502.17591},
year={ 2025 }
}