PrivatePool:保护隐私的拼车服务

Per A. Hallgren, Claudio Orlandi, A. Sabelfeld
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引用次数: 49

摘要

最近几年,基于位置的服务有了巨大的发展。Uber、Lyft、BlaBlaCar等公司的成功见证了这些服务彻底改变了交通运输行业。然而,从隐私的角度来看,目前的技术水平还有很多需要改进的地方。用户的位置通常与服务共享,就像最近公布的一些案例一样,为滥用隐私打开了方便之门。本文提出了一种保护隐私的拼车模型PrivatePool。我们为端点和轨迹匹配开发了安全的多方计算技术,允许对第三方的信任。与此同时,用户只知道他们可以分享的乘车片段,而不知道其他用户的位置。我们建立了正式的隐私保证,并研究了不同的骑行模式如何影响基于端点接近度和轨迹接近度的方法之间的隐私、效用和性能权衡。
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PrivatePool: Privacy-Preserving Ridesharing
Location-based services have seen tremendous developments over the recent years. These services have revolutionized transportation business, as witnessed by the success of Uber, Lyft, BlaBlaCar, and the like. Yet from the privacy point of view, the state of the art leaves much to be desired. The location of the user is typically shared with the service, opening up for privacy abuse, as in some recently publicized cases. This paper proposes PrivatePool, a model for privacy-preserving ridesharing. We develop secure multi-party computation techniques for endpoint and trajectory matching that allow dispensing with trust to third parties. At the same time, the users learn of a ride segment they can share and nothing else about other users' location. We establish formal privacy guarantees and investigate how different riding patterns affect the privacy, utility, and performance trade-offs between approaches based on the proximity of endpoints vs. proximity of trajectories.
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