RUPT-FL: Robust Two-Layered Privacy-Preserving Federated Learning Framework With Unlinkability for IoV
Jinhai Zhang, Junwei Zhang, Zhuo Ma, Teng Li, Xinghua Li, Jianfeng Ma · IEEE Transactions on Vehicular Technology · 2024
Privacy-preserving federated learning framework has been widely applied in the Internet of Vehicles (IoV) scenario, to enhance the privacy of local models and sensitive vehicle data. However, as increasing cyberattacks are launched against RSUs and servers, such as node compromising and data inference attacks, it is necessary to improve the robustness of uploading local updates and prevent servers from obtaining additional private information of vehicles in the two-layered IoV model, while existing schemes hardly meet all the above-mentioned security requirements. Therefore, we propose a robust and unlinkable privacy-preserving federated learning framework RUPT-FL, that tolerates RSU dropout while disassociating local updates from the RSUs who gathered them. Based on packed secret sharing, secure multi-party computation, and homomorphic encryption techniques, we design a robust and lightweight multi-RSUs-aided local update upload protocol for vehicles, then construct an identity-oblivious update reconstruction and aggregation protocol under the two-server model. We conduct a series of experiments to demonstrate that the proposed framework not only improves the robustness and accuracy of federated learning against a semi-honest adversary, but also avoids expensive computational overhead for vehicles.