A Comprehensive Framework to Enhance the Location Privacy for Geospatial Query Resolution
D.S.Eunice Little Dani, Karunakaran Rohini · 2024
The rapid expansion of geospatial data analysis has led to an increasing need for systems that can respond quickly. Storing and analyzing extensive spatiotemporal datasets is crucial for extracting valuable information for location-aware applications like route planning and weather forecasts. Efficiently coordinating data sources and internet services is crucial for extracting results within time constraints, given the current surge in processing geographic searches. Privacy models that incorporate fog computing provide a significant advantage in terms of cost reduction and low latency. However, in order to address consumers' concerns about data exposure, recent efforts have been made to integrate fog computing for enhanced privacy. Through the use of simulated outcomes, it has been shown that the privacy protection offered by the Geo-Fog framework for localization services is highly effective. The system demonstrated average latency values as low as 23 ms for query loads of 200. Furthermore, the CPU usage remained below $37 \%$ even under the maximum query load of $\mathbf{5 0 0}$. Fog computing plays a crucial role in the advancement of location-based applications that prioritize efficiency and privacy. By enabling fog devices or servers to handle geospatial queries within the geographic environment, it ensures optimal management.