Proactive Privacy Amnesia for Large Language Models: Safeguarding PII with Negligible Impact on Model Utility
Martin Kuo Jingyang Zhang Jianyi Zhang Minxue Tang Louis DiValentin Aolin Ding Jingwei Sun William Chen Amin Hass Tianlong Chen Yiran Chen Hai Li
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.
Large language models, Personal identifiable information, Protect private data leakage