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

Shahad Alkamli¹, Reham Alabduljabbar¹

¹Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh, 11362, Saudi Arabia.

|December 6, 2024

Summary

This study on ChatGPT privacy risks found users are most concerned about unauthorized access and data exploitation. Understanding these generative AI privacy concerns is crucial for improving AI security and user trust.

Keywords:

ChatGPT Data categorization Data exploitation Generative AI Latent dirichlet allocation (LDA)Personal input Privacy concerns Survey Twitter Unauthorized access

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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.