Raising Awareness of Location Information Vulnerabilities in Social Media Photos using LLMs
Open accessAbstract
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.
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Providing users with more explicit choices and consent can enhance their agency and control.
“Alternatively, providing users with more explicit choices and consent can enhance their agency and control [23,25].”
From Understanding Users' Privacy Perceptions Towards LLM's RAG-based Memory (2025) · cites this paper as [23]
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- Authors
- Ma, Y., Zhang, S., Yang, D., Sarsenbayeva, Z., Knibbe, J., & Goncalves, J.
- Affiliations
- The University of Melbourne (AU)
- The University of Sydney (AU)
- The University of Queensland (AU)
- School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia
- Venue
- Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2025-04-25
- Pages
- 1-14
- OpenAlex
- W4409736232
- Cited by
- 5
- References
- 65
- Citations by year
- 2026: 2 · 2025: 3
- FWCI
- 16.80
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Topics
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- location privacy, privacy leakage, social media images, large language models, user awareness, visual landmarks, privacy-preserving techniques
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