Building Privacy-Preserving and Secure Geospatial Artificial Intelligence Foundation Models

TL;DR

This paper discusses the privacy and security challenges of geospatial AI foundation models and proposes a comprehensive framework for developing privacy-preserving and secure GeoAI models.

Contribution

It introduces a detailed analysis of privacy and security risks in GeoAI foundation models and offers a strategic blueprint for future research and mitigation strategies.

Findings

Identifies key privacy and security risks in GeoAI models

Proposes a comprehensive research blueprint for secure GeoAI development

Raises awareness among researchers and policymakers about GeoAI security concerns

Abstract

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.