LLMs are Privacy Erasable

ACL ARR 2025 May Submission335 Authors

Abstract: The capabilities of large language models (LLMs) are advancing at an remarkable pace, along with a surge in cloud services that are powered by LLMs. Their convenience has gradually transformed the routines people work. However, for services such as document summarizing, editing, and so on, users need to upload relevant files or context to obtain the desired services, which may inadvertently expose their privacy. This paper aims to address the challenging balance between the convenience of LLMs services and user privacy concerns. Specifically, based on the structural and functional characteristics of LLMs, we have developed a strategy that safeguards user prompt while accessing LLM cloud services, even in scenarios where advanced reconstruction attacks are adopted. We comprehensively evaluate the efficacy of our method across prominent LLM benchmarks. The empirical results show that our method not only effectively thwarts reconstruction attacks but also, in certain tasks, even improves model performance, surpassing the outcomes reported in official model cards.

Paper Type: Long

Research Area: Human-Centered NLP

Research Area Keywords: large language model, cloud service, reconstruction attack, privacy preserving

Contribution Types: NLP engineering experiment

Languages Studied: English

Keywords: Large Language Models, AI Security

Submission Number: 335