Learning to Refuse: Towards Mitigating Privacy Risks in LLMs
Large language models (LLMs) exhibit remarkable capabilities in understanding
and generating natural language. However, these models can inadvertently
memorize private information, posing significant privacy risks. This study
addresses the challenge of enabling LLMs to protect specific individuals'
private data without the need for complete retraining. We propose \return, a
Real-world pErsonal daTa UnleaRNing dataset, comprising 2,492 individuals from
Wikipedia with associated QA pairs, to evaluate machine unlearning (MU) methods
for protecting personal data in a realistic scenario. Additionally, we
introduce the Name-Aware Unlearning Framework (NAUF) for Privacy Protection,
which enables the model to learn which individuals' information should be
protected without affecting its ability to answer questions related to other
unrelated individuals. Our extensive experiments demonstrate that NAUF achieves
a state-of-the-art average unlearning score, surpassing the best baseline
method by 5.65 points, effectively protecting target individuals' personal data
while maintaining the model's general capabilities.
66960be0659a52fa2757ae13
Zhenhua Liu
claimed_verified
2024-07-18T09:07:57.365Z
619b00dd4b0db5ca9d3ea35f
/avatars/fce5ac388b7f10cbbc63e9992a5a799f.svg
Zhenhua Liu
user
zhliu
66960be0659a52fa2757ae14
Tong Zhu
claimed_verified
2024-07-16T20:15:23.759Z
629454301ae2138079f7ff31
https://cdn-avatars.huggingface.co/v1/production/uploads/629454301ae2138079f7ff31/rVtbF-j06gDiYzomTeVTc.jpeg
Tong Zhu
user
Spico
66960be0659a52fa2757ae15
Chuanyuan Tan
66960be0659a52fa2757ae16
Wenliang Chen
Learning to Refuse: Towards Mitigating Privacy Risks in LLMs
Large language models (LLMs) exhibit remarkable capabilities in understanding
and generating natural language. However, these models can inadvertently
memorize private information, posing significant privacy risks. This study
addresses the challenge of enabling LLMs to protect specific individuals'
private data without the need for complete retraining. We propose \return, a
Real-world pErsonal daTa UnleaRNing dataset, comprising 2,492 individuals from
Wikipedia with associated QA pairs, to evaluate machine unlearning (MU) methods
for protecting personal data in a realistic scenario. Additionally, we
introduce the Name-Aware Unlearning Framework (NAUF) for Privacy Protection,
which enables the model to learn which individuals' information should be
protected without affecting its ability to answer questions related to other
unrelated individuals. Our extensive experiments demonstrate that NAUF achieves
a state-of-the-art average unlearning score, surpassing the best baseline
method by 5.65 points, effectively protecting target individuals' personal data
while maintaining the model's general capabilities.
The Name-Aware Unlearning Framework for Privacy Protection demonstrates superior capabilities in protecting individual private data in large language models without needing complete retraining.
LLMs large language models machine unlearning Real-world pErsonal daTa UnleaRNing Name-Aware Unlearning Framework (NAUF)