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Achieving Blockchain-based Privacy-Preserving Location Proofs under Federated Learning

机译：在联合学习下实现基于区块的隐私保留位置证明

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摘要

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.
机译：基于联合的基于学习的导航在车辆物联网中受到了很多关注。目的是沿着不同的轨迹雇用大量的最终用户进行数据收集，并执行全球学习模型的本地培训，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位于依赖于依赖的输入数据可靠性和参与者的隐私保存。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留的定位机制。具体地，所提出的方案利用基于阈值的基于身份的加密（TIBE）系统，用于产生秘密份额，使得只能用至少阈值数量的参与者验证每个匿名位置。此外，该方案还利用了咕咕滤波器，以确保安全有效的维护和传播定位。进行系统安全分析以展示苛刻的安全要求的实现。执行性能评估以验证与无知转移（OT）协议的计算效率，这些协议已被广泛采用安全数据采集。

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