In this paper, we propose a novel approach to regularize the distribution of within-class features toeaselabelpropagation difficulty.

The rapid advancement and widespread use of large language models (LLMs) have raised significant concerns regarding the potential leakage of personally identifiable information (PII). These models are often trained on vast quantities of web-collected data, which may inadvertently include sensitive personal data.