With the quick development of the sharing economy, ride-hailing services have been increasingly popular worldwide. Although the service provides convenience for users, one concern from the public is whether the location privacy of passengers would be protected. Service providers (SPs) such as Didi and Uber need to acquire passenger and driver locations before they could successfully dispatch passenger orders. To protect passengers’ privacy based on their requirements, we propose a cloaking region based order dispatch scheme. In our scheme, a passenger sends the SP a cloaking region in which his/her actual location is not distinguishable. The trade-off of the enhanced privacy is the loss of social welfare, i.e., the increase in the overall pick-up distance. To optimize our scheme, we propose to maximize the social welfare under passengers’ privacy requirements. We investigate a bipartite matching based approach. A theoretical bound on the matching performance under specific privacy requirements is shown. Besides passengers’ privacy, we allow drivers to set up their maximum pick-up distance in our extended scheme. The extended scheme could be applied when the number of drivers exceeds the number of passengers. Nevertheless, the global matching based scheme does not consider the interest of each individual passenger. The passengers with low privacy requirements may be matched with drivers far from them. To this end, a pricing scheme including three strategies is proposed to make up for the individual loss by allocating discounts on their riding fares. Extensive experiments on both real-world and synthetic datasets show the efficiency of our scheme.

中文翻译：

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乘车系统中基于伪装区域的乘客隐私保护

随着共享经济的快速发展，网约车服务在全球范围内越来越受欢迎。尽管该服务为用户提供了便利，但公众关心的一个问题是乘客的位置隐私是否会受到保护。滴滴和优步等服务提供商 (SP) 需要先获取乘客和司机的位置，然后才能成功派送乘客订单。为了根据乘客的要求保护他们的隐私，我们提出了一种基于隐藏区域的订单调度方案。在我们的方案中，乘客向 SP 发送一个隐藏区域，在该区域中他/她的实际位置无法区分。增强隐私的权衡是社会福利的损失，即总体上车距离的增加。为了优化我们的方案，我们建议在乘客隐私要求下最大化社会福利。我们研究了一种基于二分匹配的方法。显示了特定隐私要求下匹配性能的理论界限。除了乘客的隐私，我们还允许司机在我们的扩展计划中设置他们的最大上车距离。当司机人数超过乘客人数时，可以应用扩展计划。然而，基于全局匹配的方案没有考虑每个乘客的兴趣。对隐私要求不高的乘客可能会与距离他们较远的司机相匹配。为此，提出了一种包含三种策略的定价方案，通过分配乘车费用折扣来弥补个人损失。