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Due to the limited battery and computing resource, offloading unmanned aerialvehicles (UAVs)' computation tasks to ground infrastructure, e.g., vehicles, isa fundamental framework. Under such an open and untrusted environment, vehiclesare reluctant to share their computing resource unless provisioning strongincentives, privacy protection, and fairness guarantee. Precisely, withoutstrategy-proofness guarantee, the strategic vehicles can overclaimparticipation costs so as to conduct market manipulation. Without the fairnessprovision, vehicles can deliberately abort the assigned tasks without anypunishments, and UAVs can refuse to pay by the end, causing an exchangedilemma. Lastly, the strategy-proofness and fairness provision typicallyrequire transparent payment/task results exchange under public audit, which maydisclose sensitive information of vehicles and make the privacy preservation aforemost issue. To achieve the three design goals, we propose SEAL, anintegrated framework to address strategy-proof, fair, and privacy-preservingUAV computation offloading. SEAL deploys a strategy-proof reverse combinatorialauction mechanism to optimize UAVs' task offloading under practical constraintswhile ensuring economic-robustness and polynomial-time efficiency. Based onsmart contracts and hashchain micropayment, SEAL implements a fair on-chainexchange protocol to realize the atomic completion of batch payments andcomputing results in multi-round auctions. In addition, a privacy-preservingoff-chain auction protocol is devised with the assistance of the trustedprocessor to efficiently protect vehicles' bid privacy. Using rigoroustheoretical analysis and extensive simulations, we validate that SEAL caneffectively prevent vehicles from manipulating, ensure privacy protection andfairness, improve the offloading efficiency.

SEAL: A Strategy-Proof and Privacy-Preserving UAV Computation Offloading Framework