Enhancing Data Privacy in Large Language Models through Private Association Editing
This addresses data privacy concerns for users of LLMs in data-intensive applications, though it appears incremental as a defense approach building on existing methods.
The paper tackles the problem of large language models (LLMs) memorizing and leaking private information by introducing Private Association Editing (PAE) to remove Personally Identifiable Information (PII) without retraining, with experimental results showing its effectiveness compared to baseline methods.
Large language models (LLMs) require a significant redesign in solutions to preserve privacy in data-intensive applications due to their text-generation capabilities. Indeed, LLMs tend to memorize and emit private information when maliciously prompted. In this paper, we introduce Private Association Editing (PAE) as a novel defense approach for private data leakage. PAE is designed to effectively remove Personally Identifiable Information (PII) without retraining the model. Experimental results demonstrate the effectiveness of PAE with respect to alternative baseline methods. We believe PAE will serve as a critical tool in the ongoing effort to protect data privacy in LLMs, encouraging the development of safer models for real-world applications.