Privacy Control in Conversational LLM Platforms: A Walkthrough Study

TL;DR

This paper analyzes how six conversational LLM platforms implement user data control features, revealing an emerging paradigm of privacy management that involves natural language and shared data complexities.

Contribution

It provides a systematic analysis of data control mechanisms in conversational LLM platforms and offers practical insights for improving privacy controls.

Findings

Data control features vary across platforms.

Natural language introduces ambiguity in user control.

Shared data raises co-ownership and governance issues.

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.