Raising Awareness of Location Information Vulnerabilities in Social Media Photos using LLMs

2025-03-26

AI Summary

Visual cues embedded in social media photos can be exploited by large language models (LLMs) to infer precise geographic locations, posing serious privacy risks—including tracking and identity theft. Method: We propose a cognition-centered privacy-preserving paradigm: designing and deploying the first intervention application that leverages LLMs as interpretable threat demonstrators. The system performs image semantic analysis and delivers interactive, explainable prompts to help users identify location-leaking visual features. Contribution/Results: Through qualitative interviews and a two-week user study (N=19), we demonstrate significant improvements in participants’ location privacy awareness and critical reflection on privacy agency. We further distill empirically grounded design principles for user-facing privacy-enhancing technologies, advancing privacy protection from passive defense toward active, cognitively supported self-regulation.

Technology Category

• Machine Learning: Privacy
• Computer Vision: Bias, Fairness & Privacy
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Security and Privacy: Privacy-enhancing technologies
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Location privacy leaks can lead to unauthorised tracking, identity theft, and targeted attacks, compromising personal security and privacy. This study explores LLM-powered location privacy leaks associated with photo sharing on social media, focusing on user awareness, attitudes, and opinions. We developed and introduced an LLM-powered location privacy intervention app to 19 participants, who used it over a two-week period. The app prompted users to reflect on potential privacy leaks that a widely available LLM could easily detect, such as visual landmarks&cues that could reveal their location, and provided ways to conceal this information. Through in-depth interviews, we found that our intervention effectively increased users' awareness of location privacy and the risks posed by LLMs. It also encouraged users to consider the importance of maintaining control over their privacy data and sparked discussions about the future of location privacy-preserving technologies. Based on these insights, we offer design implications to support the development of future user-centred, location privacy-preserving technologies for social media photos.

Problem

• Addressing location privacy leaks in social media photos using LLMs
• Enhancing user awareness of LLM-powered location data vulnerabilities
• Developing interventions to mitigate location privacy risks in photo sharing

Innovation

• LLM-powered app detects photo location leaks
• App prompts user reflection on privacy risks
• Interviews show increased location privacy awareness