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

--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. T o 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. Large Language Models (LLMs) like ChatGPT have revolutionized digital interactions by providing personalized, context-aware responses that evolve with the dialogue. Unlike traditional information sources, LLMs' dynamic engagement often leads users to share increasingly personal details over multiple sessions, sometimes unknowingly. This gradual accumulation of sensitive information, compounded by the public's limited understanding of risks like neural network memorization, increases the likelihood of unintentional disclosure. The issue is further exacerbated when proprietary LLMs operate in jurisdictions with weak privacy regulations, limited data security, or invasive governmental surveillance.