Building Privacy-Preserving and Secure Geospatial Artificial Intelligence Foundation Models
- Autor(en)
- Jinmeng Rao, Song Gao, Gengchen Mai, Krzysztof Janowicz
- Abstrakt
In recent years we have seen substantial advances in foundation models for artificial intelligence, including language, vision, and multimodal models. Recent studies have highlighted the potential of using foundation models in geospatial artificial intelligence, known as GeoAI Foundation Models, for geographic question answering, remote sensing image understanding, map generation, and location-based services, among others. However, the development and application of GeoAI foundation models can pose serious privacy and security risks, which have not been fully discussed or addressed to date. This paper introduces the potential privacy and security risks throughout the lifecycle of GeoAI foundation models and proposes a comprehensive blueprint for research directions and preventative and control strategies. Through this vision paper, we hope to draw the attention of researchers and policymakers in geospatial domains to these privacy and security risks inherent in GeoAI foundation models and advocate for the development of privacy-preserving and secure GeoAI foundation models.
- Organisation(en)
- Institut für Geographie und Regionalforschung
- Externe Organisation(en)
- University of Wisconsin, Madison, University of Georgia, University of California, Santa Barbara
- Seiten
- 1-4
- DOI
- https://doi.org/10.1145/3589132.3625611
- Publikationsdatum
- 11-2023
- Peer-reviewed
- Ja
- ÖFOS 2012
- 105403 Geoinformatik, 102001 Artificial Intelligence
- Schlagwörter
- ASJC Scopus Sachgebiete
- Earth-Surface Processes, Information systems, Computer Science Applications, Modelling and Simulation, Computer Graphics and Computer-Aided Design
- Link zum Portal
- https://ucrisportal.univie.ac.at/de/publications/27f5e01a-bd9f-4f80-8433-c6223945c6d1