A Privacy-Preserving Auction for Task Offloading and Resource Allocation in UAV-Assisted MEC

As a complementary solution for Mobile Edge Computing (MEC), Unmanned Aerial Vehicles (UAVs) can temporarily provide reliable and flexible offloading services when edge servers are damaged or unavailable. However, existing UAV-assisted MEC systems suffer from issues such as uneven resource allocation, low utilization efficiency, load imbalance, and poor dynamic adaptability, affecting service quality. Moreover, sensitive user equipment (UE) information faces leakage during the computational process of UAVs. How to jointly optimize the scheduling of servers and UAVs for task offloading and resource allocation without compromising UEs' privacy remains a significant challenge. Thus, this paper proposed a privacy-preserving auction framework (namely Prizty) by considering the trajectory of UAVs, their constrained energy and computational capabilities, and the variability in UE distribution. Prizty employs a combinatorial obfuscation method to protect UEs' privacy and links bidding prices to computational resources and energy characteristics. It calls the sub-algorithm WPA to determine the winners by balancing social costs and utility. Theoretical analysis demonstrates that Prizty satisfies truthfulness and individual rationality while maintaining scalability for large-scale resource allocation problems. Extensive experiments on real-world datasets validate Prizty's effectiveness in critical metrics, including offload rate, average service latency, energy consumption, and social cost.