# GeoLocator: A Location-Integrated Large Multimodal Model (LMM) for Inferring Geo-Privacy
## 1 Introduction  
The advent of the internet, big data, and generative AI has significantly transformed the field of geosciences, offering unprecedented opportunities for collecting, analyzing, disseminating, and generating geographic information [1, 2]. In today's digital era, the risk of personal information being quietly leaked is a growing concern, with geographic privacy becoming a focal point. The integrity of geographic data is not only a matter of personal privacy but also a cornerstone of sustainable geoscience practices. Ensuring the privacy of geographic information prevents misuse and distortion, which is crucial for maintaining the trust and accuracy necessary for sustainable geoscience endeavors. Geographic privacy involves the protection and confidentiality of geographic information related to individuals. It primarily includes safeguarding data that disclose personal geographic locations, such as real-time whereabouts, historical movement patterns, or any location-specific information traceable to an individual [3]. Geographic privacy is critical; however, in an era where smartphones and social media platforms are ubiquitous, maintaining this privacy is a significant challenge. While services like navigation, travel booking websites, and social media offer convenience, they also pose potential risks to our geographic privacy throughpotential surveillance, unauthorized data mining, and third-party misuse [4]. The existing legal frameworks struggle to keep pace with the rapidly evolving technologies that threaten geographic privacy, further exacerbating these concerns [5]. In the current era of rapid advancements in generative AI, we have identified a commonly overlooked method of geographic privacy leakage: our photos often contain substantial geographic information. In the past, the information embedded in these photos might not have been easily captured [3]. However, today, consider a simple case on social media: you post a photo of yourself visiting a baseball stadium. From this photo, one can infer your geographic location, specifically which stadium it is and its exact address. A single photo can compromise your geographic privacy. With the rapid development of large multimodal models like GPT-4, these models can extract, interpret, and infer geographic information from your posted images. GPT-4's ability to infer details from photos poses a significant threat to geographic privacy [6]. These models can directly reveal precise location details from geotagged images or indirectly through contextual analysis. The potential harm and implications are profound and multifaceted, including identity theft, personal security breaches, and serious intrusions into personal privacy. Recognizing the potential threat posed by GPT-4 to geographic privacy, we developed GeoLocator, a tool that integrates GPT-4 with geolocation capabilities to demonstrate its ability to infer location information from input images and social media content. To evaluate and compare the privacy attack capabilities of conventional search engines, GPT-4, and GeoLocator, we designed a series of experiments based on inputs from various datasets, including Google Maps images, day/night images, and social media posts. Our experiments demonstrate that GeoLocator can generate specific geographic details with high accuracy, thereby embedding the risk of users unintentionally exposing geospatial information to the public. This underscores the themes of online data sharing, information collection technologies, and geographic privacy jurisprudence. Our findings emphasize the urgency for enhanced awareness and protective measures against geographic privacy leakage in the era of advanced AI and widespread social media usage. By combining these insights with robust privacy protection practices, we aim to ensure that the benefits of geoscience advancements do not come at the expense of individual rights or societal trust.