SA-ADP: Sensitivity-Aware Adaptive Differential Privacy for Large Language Models

TL;DR

SA-ADP introduces a sensitivity-aware differential privacy method for large language models, balancing privacy and utility by allocating noise based on PII sensitivity, and achieves comparable performance to non-private models.

Contribution

This paper presents a novel sensitivity-aware differential privacy approach that improves privacy protection without sacrificing model utility in large language models.

Findings

SA-ADP maintains model utility comparable to non-private training.

SA-ADP provides strong privacy protection by allocating noise based on PII sensitivity.

The method performs well across multiple datasets, demonstrating robustness.

Abstract

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.