驾驶员位置收集对pRide的攻击
Driver Locations Harvesting Attack on pRide
摘要：
网约车服务（RHS）中的隐私保护旨在保护司机和乘客的隐私。pRide发表在IEEE Trans. Vehicular Technology 2021上，是一种基于预测的隐私保护RHS协议，用于将乘客与最佳驾驶员相匹配。在该协议中，服务提供商（SP）同态计算驾驶员和乘客的加密位置之间的欧几里得距离。Rider使用由新的乘车出现预测增强的解密距离来选择最佳驾驶员。为了提高驾驶员选择的有效性，本文提出了一个增强版本，其中每个驾驶员提供到其网格的每个角的加密距离。为了阻止骑手使用这些距离来发起推理攻击，SP在与骑手共享这些距离之前将这些距离设为盲。在这项工作中，我们提出了一个被动的攻击，一个诚实但好奇的对手骑手谁提出了一个单一的乘坐请求，并从SP接收盲距离可以恢复常数用于盲的距离。使用非盲距离、车手与驾驶员的距离和Google Nearest Road API，对手可以获得响应驾驶员的精确位置。我们进行实验，随机对道路上的司机位置为四个不同的城市。我们的实验表明，我们可以确定至少80%的司机参与增强型pRide协议的精确位置。
英文摘要：
Privacy preservation in Ride-Hailing Services (RHS) is intended to protect privacy of drivers and riders. pRide, published in IEEE Trans. Vehicular Technology 2021, is a prediction based privacy-preserving RHS protocol to match riders with an optimum driver. In the protocol, the Service Provider (SP) homomorphically computes Euclidean distances between encrypted locations of drivers and rider. Rider selects an optimum driver using decrypted distances augmented by a new-ride-emergence prediction. To improve the effectiveness of driver selection, the paper proposes an enhanced version where each driver gives encrypted distances to each corner of her grid. To thwart a rider from using these distances to launch an inference attack, the SP blinds these distances before sharing them with the rider. In this work, we propose a passive attack where an honest-but-curious adversary rider who makes a single ride request and receives the blinded distances from SP can recover the constants used to blind the distances. Using the unblinded distances, rider to driver distance and Google Nearest Road API, the adversary can obtain the precise locations of responding drivers. We conduct experiments with random on-road driver locations for four different cities. Our experiments show that we can determine the precise locations of at least 80% of the drivers participating in the enhanced pRide protocol.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
