Ride-on-demand (RoD) services, which are prime examples of crowdsensing applications, deliver critical functionalities such as dynamic pricing and ride-matching to users. In this chapter, we focus on RoD services under a semi-honest server and propose the Pricing-Aware Differentially Private framework (PADP-RoD). This framework is designed to protect users’ location privacy while still providing top-notch location-based services. Recognizing that price multipliers can fluctuate abruptly in response to supply and demand shifts, especially in hotspot areas, we propose an adaptive supply and demand aware grid to capture these changes. Building on this grid, we propose two utility metrics that quantify the service quality degradation in dynamic pricing and ride-matching caused by the necessary noise perturbations. With these metrics, PADP-RoD is formulated as a minimization problem, aiming to reduce service quality loss while satisfying differential privacy constraints. By this problem, we can obtain an optimal balance between privacy preservation and service quality. Extensive privacy analyses and performance evaluations confirm that PADP-RoD can deliver high-quality dynamic pricing and ride-matching services while protecting users’ location privacy.