Malki, Lisa Mekioussa;
Polamarasetty, akhil;
Hatamian, majid;
Costanza, enrico;
Warner, mark;
(2025)
``Hoovered up as a data point'': Exploring Privacy Behaviours, Awareness, and Concerns Among UK Users of LLM-based Conversational Agents.
In:
Proceedings on Privacy Enhancing Technologies 2025(3).
ACM
(In press).
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Abstract
Large Language Models (LLMs) are widely used in conversational agents (CAs) due to their ability to generate coherent and humanlike text. However, their deployment raises significant privacy concerns, as users often share sensitive data in prompts. This data can be used to train the underlying LLM, introducing memorisation risks and challenging users’ right to be forgotten. While these issues have been explored from a technical standpoint, little is known about how users perceive and navigate privacy issues in their dayto-day use of LLM-based CAs. To address this research gap, we conducted a survey of UK-based CA users (
Type: | Proceedings paper |
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Title: | ``Hoovered up as a data point'': Exploring Privacy Behaviours, Awareness, and Concerns Among UK Users of LLM-based Conversational Agents |
Event: | Privacy Enhancing Technologies Symposium |
Location: | Washington DC |
Dates: | 14 Jul 2025 - 19 Jul 2025 |
Open access status: | An open access version is available from UCL Discovery |
Language: | English |
Additional information: | This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license visit https://creativecommons.org/licenses/by/4.0/ or send a letter to Creative Commons. |
Keywords: | Large Language Models (LLMs), AI, conversational interfaces, chatbots, usable privacy, human-computer interaction |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/10210557 |
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