【Author】
Kong, Qinglei; Yin, Feng; Xiao, Yue; Li, Beibei; Yang, Xuejia; Cui, Shuguang
【Source】IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2021)
【Abstract】Federated learning-based navigation has received much attention in vehicular IoT. The intention is to employ a big number of end-users for data collection along different trajectories and perform local training of a global learning model to substitute the global positioning system (GPS) in urban areas. The prerequisites for its commercialization, however, lie in the location-dependent input data trustworthiness and participants' privacy preservation. In this paper, we propose a privacy-preserving proof-of-location mechanism using blockchain to meet these conditions. Specifically, the proposed scheme utilizes a Threshold Identity-Based Encryption (TIBE) system for the generation of secret shares, such that each anonymous location proof can only be verified with at least a threshold number of participants. In addition, the proposed scheme exploits a cuckoo filter for the secure and efficient maintenance and dissemination of location proofs. Systematic security analysis is conducted to demonstrate the fulfillment of harsh security requirements. Performance evaluations are carried out to validate the computation efficiency in comparison with an oblivious transfer (OT) protocol, which has been widely adopted for secure data acquisition.
【Keywords】Federated Learning; Privacy Preservation; Location Proof; Navigation
【标题】在联邦学习下实现基于区块链的隐私保护位置证明
【摘要】基于联邦学习的导航在车载物联网中备受关注。其目的是雇佣大量最终用户沿不同轨迹收集数据，并对全球学习模型进行本地训练，以替代城市地区的全球定位系统 (GPS)。然而，其商业化的先决条件在于依赖于位置的输入数据的可信度和参与者的隐私保护。在本文中，我们提出了一种使用区块链来满足这些条件的隐私保护位置证明机制。具体来说，所提出的方案利用基于阈值身份的加密 (TIBE) 系统来生成秘密共享，这样每个匿名位置证明只能通过至少阈值数量的参与者进行验证。此外，所提出的方案利用布谷鸟过滤器来安全有效地维护和传播位置证明。进行系统的安全分析以证明满足严格的安全要求。与已广泛用于安全数据采集的不经意传输 (OT) 协议相比，进行了性能评估以验证计算效率。
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