A Hybrid Differential Privacy and k-Anonymity Framework for Enhancing Location Privacy in Location-Based Services

The growing reliance on Location-Based Services (LBS) has intensified privacy risks, as the continuous collection of sensitive user location data exposes individuals to potential re-identification and unauthorised tracking. This paper presents a hybrid privacy-preserving framework that combines the Diameter-Bounded DBSCAN clustering algorithm for spatial k-anonymity with an adaptive Laplace mechanism for ε-differential privacy. This integration ensures the formation of compact anonymity groups while maintaining high data utility. Experimental evaluation on the real-world GeoLife dataset demonstrates 85.1% query accuracy, 0.14 trajectory distortion (EDR), and average query latency below 100 milliseconds for 20,000 users, outperforming DPPS and AdaptiveGrid baselines. Comprehensive sensitivity analysis of the diameter threshold (dmax) and evaluation of suppression bias confirm the framework’s robustness, scalability, and practical suitability for real-time LBS deployment.