Guarding Your Conversations: Privacy Gatekeepers for Secure Interactions with Cloud-Based AI Models

TL;DR

This paper introduces a privacy gatekeeper system that filters sensitive information locally before interacting with cloud-based LLMs, significantly improving user privacy with minimal performance impact.

Contribution

It proposes a novel dual-model architecture that enhances privacy by filtering user data locally prior to cloud-based LLM interactions, addressing privacy risks in untrusted jurisdictions.

Findings

Significant reduction in sensitive data exposure.

Minimal response quality degradation.

Effective privacy enhancement demonstrated with human subjects.

Abstract

The interactive nature of Large Language Models (LLMs), which closely track user data and context, has prompted users to share personal and private information in unprecedented ways. Even when users opt out of allowing their data to be used for training, these privacy settings offer limited protection when LLM providers operate in jurisdictions with weak privacy laws, invasive government surveillance, or poor data security practices. In such cases, the risk of sensitive information, including Personally Identifiable Information (PII), being mishandled or exposed remains high. To address this, we propose the concept of an "LLM gatekeeper", a lightweight, locally run model that filters out sensitive information from user queries before they are sent to the potentially untrustworthy, though highly capable, cloud-based LLM. Through experiments with human subjects, we demonstrate that this dual-model approach introduces minimal overhead while significantly enhancing user privacy, without compromising the quality of LLM responses.