Unmanned Aerial Vehicles (UAVs) have gained great attention in Internet-of-Things (IoT) applications benefiting from the flexibility of deployment and line-of-sight (LoS) channel conditions. In this paper, we study a UAV-assisted Mobile Edge Computing (MEC) system for providing services to large-scale IoT nodes (INs). In the considered system, the UAV acts as an Aerial Base Station (ABS) that can selectively access large-scale INs to enable efficient data collection and computational offloading while ensuring data integrity. Specifically, we first derive the reconstruction error upper bound based on Graph Laplacian Regularization (GLR) as the data integrity metric. Considering that the UAV is usually limited in energy consumption, we propose an energy efficiency (EE) maximization problem that jointly optimizes the selection of INs, the scheduling of INs, the 3D flight and the computational resource allocation of the UAV, subject to constraints related to UAV motion, resources and data integrity. Due to the non-convex nature of the considered problem, a two-stage algorithm called GDA-3DNACRA is proposed, which adopts Gershgorin Disk Alignment (GDA), Convex Relaxation, and Successive Convex Approximation (SCA) for solving it efficiently. Simulation results have shown that the proposed approach can significantly improve the EE of the UAV while ensuring the data integrity.