SA-ADP: Sensitivity-Aware Adaptive Differential Privacy for Large Language Models
This addresses privacy concerns for LLM users by improving the trade-off between privacy and utility, though it appears incremental as it builds on existing DP methods.
The paper tackled the problem of privacy protection in large language models (LLMs) by proposing SA-ADP, a sensitivity-aware adaptive differential privacy method that allocates noise based on individual PII sensitivity, achieving results comparable to baseline and DP-SGD without degrading utility.
Despite advances in the use of large language models (LLMs) in downstream tasks, their ability to memorize information has raised privacy concerns. Therefore, protecting personally identifiable information (PII) during LLM training remains a fundamental challenge. Conventional methods like Differential Privacy-Stochastic Gradient Descent (DP-SGD) provide robust privacy protection via uniform noising, protecting PII regardless of its distinct sensitivity. This comes at the expense of the model's utility, leading to a trade-off. In this paper, we propose SA-ADP, a sensitivity-aware approach that allocates noise based on the sensitivity of individual PII. We evaluated our method on four datasets (ABCD, CUSTOMERSIM, Wikitext-2, and UNSW-NB15 ). Our results show that SA-ADP achieves results comparable to the baseline (No-DP) and the conventional DP-SGD. This means that our method did not degrade the model's utility while still maintaining strong privacy protection.