  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.
