Locally Differentially Private Document Generation Using Zero Shot Prompting

TL;DR

This paper introduces DP-Prompt, a novel locally differentially private mechanism using zero-shot prompting with large language models like ChatGPT, significantly reducing de-anonymization success while maintaining utility.

Contribution

It presents a new privacy-preserving method leveraging pretrained models and zero-shot prompting, outperforming existing approaches in de-anonymization resistance.

Findings

DP-Prompt reduces author identification F1 by 46% on IMDB.

It maintains sentiment analysis accuracy while enhancing privacy.

Extensive experiments across models up to 7 billion parameters validate effectiveness.

Abstract

Numerous studies have highlighted the privacy risks associated with pretrained large language models. In contrast, our research offers a unique perspective by demonstrating that pretrained large language models can effectively contribute to privacy preservation. We propose a locally differentially private mechanism called DP-Prompt, which leverages the power of pretrained large language models and zero-shot prompting to counter author de-anonymization attacks while minimizing the impact on downstream utility. When DP-Prompt is used with a powerful language model like ChatGPT (gpt-3.5), we observe a notable reduction in the success rate of de-anonymization attacks, showing that it surpasses existing approaches by a considerable margin despite its simpler design. For instance, in the case of the IMDB dataset, DP-Prompt (with ChatGPT) perfectly recovers the clean sentiment F1 score while achieving a 46\% reduction in author identification F1 score against static attackers and a 26\% reduction against adaptive attackers. We conduct extensive experiments across six open-source large language models, ranging up to 7 billion parameters, to analyze various effects of the privacy-utility tradeoff.