Privacy Control in Conversational LLM Platforms: A Walkthrough Study

2026-02-11

AI Summary

This study addresses the widespread lack of effective user control mechanisms over generated data in current conversational large language model platforms, which leads to ambiguous privacy management and governance challenges. Through a qualitative walkthrough analysis, the research systematically examines how six mainstream platforms implement data access, editing, deletion, and sharing functionalities. It identifies a novel data control paradigm centered on natural language interaction, highlighting the inherent tension between its flexibility and potential ambiguity. Furthermore, the work presents the first exploration of data co-governance challenges in multi-user scenarios. The findings offer actionable recommendations for developers, policymakers, and researchers to enhance the usability and effectiveness of privacy controls in conversational AI systems.

Technology Category

• Natural Language Processing: Conversational AI/Dialog Systems
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Machine Learning: Privacy

Application Category

• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Large language models (LLMs) are increasingly integrated into daily life through conversational interfaces, processing user data via natural language inputs and exhibiting advanced reasoning capabilities, which raises new concerns about user control over privacy. While much research has focused on potential privacy risks, less attention has been paid to the data control mechanisms these platforms provide. This study examines six conversational LLM platforms, analyzing how they define and implement features for users to access, edit, delete, and share data. Our analysis reveals an emerging paradigm of data control in conversational LLM platforms, where user data is generated and derived through interaction itself, natural language enables flexible yet often ambiguous control, and multi-user interactions with shared data raise questions of co-ownership and governance. Based on these findings, we offer practical insights for platform developers, policymakers, and researchers to design more effective and usable privacy controls in LLM-powered conversational interactions.

Problem

• privacy control
• conversational LLMs
• user data
• data governance
• multi-user interactions

Innovation

• privacy control
• conversational LLMs
• data governance
• user data ownership
• natural language interfaces