Advancing Location Privacy in Urban Networks: A Hybrid Approach Leveraging Federated Learning and Geospatial Semantics

Abstract

This The proliferation of location-based services (LBS) in urban networks has raised concerns about user location privacy. This paper introduces a novel framework that synergizes federated learning with geospatial semantic analysis to address these concerns. Unlike traditional centralized models, our approach ensures that sensitive user data is processed locally on users’ devices through federated learning, significantly enhancing privacy. Meanwhile, geospatial semantic analysis allows for context-aware privacy measures, adapting protections based on the semantic significance of different geographic areas. We demonstrate the effectiveness of our method through extensive experimentation, which shows that our approach can significantly improve privacy protections without diminishing the utility of LBS. Despite the promising results, we recognize the limitations imposed by network dependencies and propose future research directions to enhance the resilience of privacy-preserving mechanisms in variable network conditions. Our work contributes to the development of more secure, efficient, and user-centric location-based services, paving the way for advancements in urban network privacy.