Online ride hailing (ORH) services enable a rider to request a driver to take him wherever he wants through a smartphone app on short notice. To use ORH services, users have to submit their ride information to the ORH service provider to make ride matching, such as pick-up/drop-off location. However, the submission of ride information may lead to the leakages of users’ privacy. In this paper, we focus on the issue of protecting the location information of both riders and drivers during ride matching and propose a privacy-preserving online ride matching scheme, called pRMatch. It enables an ORH service provider to find the closest available driver for an incoming rider over a city-scale road network, while protecting the location privacy of both riders and drivers against the ORH service provider and other unauthorized participants. In pRMatch, we compute the shortest road distance over encrypted data by using road network embedding and partially homomorphic encryption and further efficiently compare encrypted distances by using ciphertext packing and shuffling. The theoretical analysis and experimental results demonstrate that pRMatch is accurate and efficient, yet preserving users’ location privacy.

中文翻译：

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向最接近的司机致敬：在线乘车服务中的隐私保护乘车匹配

在线叫车服务（ORH）使骑手可以在短时间内通过智能手机应用程序请求驾驶员将他带到任何需要的地方。要使用ORH服务，用户必须将其乘车信息提交给ORH服务提供商以进行乘车匹配，例如上车/下车位置。然而，乘坐信息的提交可能导致用户隐私的泄漏。在本文中，我们着重于在骑行匹配期间保护骑手和驾驶员的位置信息的问题，并提出了一种保护隐私的在线骑行匹配方案，称为pRMatch。它使ORH服务提供商可以在城市规模的道路网络上为即将到来的骑手找到最接近的驾驶员，同时保护骑手和驾驶员的位置隐私不受ORH服务提供商和其他未经授权的参与者的侵害。在pRMatch中，我们使用道路网络嵌入和部分同态加密来计算加密数据上的最短道路距离，并通过密文打包和混洗进一步有效地比较加密距离。理论分析和实验结果表明，pRMatch是准确有效的，但可以保护用户的位置隐私。