Understanding privacy concerns in ChatGPT: A data-driven approach with LDA topic modeling

Area of Science:

• Artificial Intelligence
• Cybersecurity
• Human-Computer Interaction

Background:

• Generative AI models like ChatGPT raise significant privacy concerns.
• Public discourse on Twitter and user surveys are key methods to gauge these concerns.
• Understanding user perceptions is vital for developing secure AI systems.

Purpose of the Study:

• To investigate and categorize privacy concerns related to ChatGPT.
• To analyze user perceptions of privacy risks in generative AI.
• To identify key areas for improving AI privacy and security.

Main Methods:

• Analysis of over 500,000 tweets related to ChatGPT privacy using Latent Dirichlet Allocation (LDA) topic modeling.
• A user survey of 67 ChatGPT users to gather direct feedback on privacy experiences.
• Data preprocessing using Python for tweet dataset refinement.

Main Results:

• Identified three primary privacy leakage areas: public data exploitation, personal input exploitation, and unauthorized access.
• Twitter data analysis and user surveys revealed significant user apprehension, especially concerning unauthorized access.
• Nuanced user perceptions highlight the need for enhanced AI privacy measures.

Conclusions:

• Generative AI systems require robust privacy measures to address user concerns.
• Findings inform AI developers, policymakers, and researchers on managing AI privacy threats.
• This research contributes to a better understanding of privacy in the evolving landscape of artificial intelligence.