Although mobile apps and location-based services provide customers with until unheard-of convenience and personalizing, they seriously compromise their location privacy. In the framework of location-based services, this study tackles the crucial difficulty of harmonizing privacy protection with data utility. Including both point-based and set-based differential privacy strategies, we offer a thorough evaluation of several Location Privacy Preserving Approaches. We assess these LPPAs over a range of privacy budgets. We provide a new Polar mechanism with adaptive noise using location density to dynamically change noise levels, hence minimizing the trade-off between privacy and accuracy. We show that our proposed approach regularly beats baseline techniques in terms of spatial inaccuracy and root mean square error. Detailed empirical studies show that these cutting-edge methods preserve strong privacy protection and improve accuracy.