Controlling What You Share: Assessing Language Model Adherence to Privacy Preferences

TL;DR

This paper presents a framework enabling users to control their data privacy in language model interactions through natural language privacy profiles, improving privacy preservation while maintaining performance.

Contribution

It introduces a novel privacy-preserving query rewriting framework using privacy profiles and the PEEP dataset, enhancing local model privacy adherence.

Findings

Fine-tuned local models better preserve privacy.

Local models match or outperform larger zero-shot models.

Challenges remain in fully understanding user privacy instructions.

Abstract

Large language models (LLMs) are primarily accessed via commercial APIs, but this often requires users to expose their data to service providers. In this paper, we explore how users can stay in control of their data by using privacy profiles: simple natural language instructions that say what should and should not be revealed. We build a framework where a local model uses these instructions to rewrite queries, only hiding details deemed sensitive by the user, before sending them to an external model, thus balancing privacy with performance. To support this research, we introduce PEEP, a multilingual dataset of real user queries annotated to mark private content and paired with synthetic privacy profiles. Experiments with lightweight local LLMs show that, after fine-tuning, they not only achieve markedly better privacy preservation but also match or exceed the performance of much larger zero-shot models. At the same time, the system still faces challenges in fully adhering to user instructions, underscoring the need for models with a better understanding of user-defined privacy preferences.