Unmasking Users in IoC: LLMs, Text Profiling, and Privacy Implications

Abstract The synthesis of Large Language Models (LLMs) with the Internet of Cloud (IoC) ecosystems creates multiple opportunities across diverse domains such as healthcare, finance, and smart cities. This study explores the combination of these technologies, focusing on their ability to profile authors and the associated privacy challenges. Two interesting experiments were conducted using real data from the Blog Authorship Corpus and the Reddit Self-Reported Depression Diagnosis (RSDD). Then the capabilities of two well-known LLMs (ChatGPT-4o and Llama 3-70B) were evaluated regarding the identification of sensitive demographic data of users such as gender, age, profession, and psychological conditions. Our findings highlight privacy risks in IoC environments, where user-authored logs, commands, and reports are already stored and analyzed by cloud-based LLM services. Moreover, key insights indicate that while LLMs improve precision and adaptability in textual data analysis, they also increase the potential risks of profiling detection in sensitive contexts. This research highlights the urgency of implementing robust privacy-preserving strategies to mitigate ethical risks and social impacts. Finally, it presents all the useful findings from the two experiments and then provides a detailed analysis of the results by comparing the two LLMs used.