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首页> 外文会议>IEEE 10th International Conference on Ubiquitous Intelligence and Computing; IEEE 10th International Conference on Autonomic and Trusted Computing >Private Map Matching: Realistic Private Route Cognition on Road Networks
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Private Map Matching: Realistic Private Route Cognition on Road Networks

机译：私人地图匹配：道路网络上现实的私人路线认知

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摘要

The rush for personalized user information, triggered by the daily generation of a staggering amount of geospatial data from multitude platforms, is leading to an erosion of users' location privacy. To ensure the privacy of moving objects on road networks, most existing works do not enforce a strict constrain that the anonymized or perturbed geospatial points should lie on the road segments. Thus, rendering the results unrealistic. In addition, humans armed with GPS-enabled devices have proven to be effective sensors which can be beneficial to traffic monitoring and other crowd sourcing services. However, they are discouraged to participate due to privacy concerns. Based on these drawbacks, we make a case for fusing privacy to map matching in road networks. In this paper, we propose a novel privacy preserving map matching technique that utilizes hidden Markov model, tangent distance and geometric properties of road segments. Our technique harnesses location privacy by first performing map matching. Then based on a defined set of a user's sensitive locations, we introduce a new cost function and employ it to determine a minimum cost alternate private route in our shortest path problem. We demonstrate using the Microsoft Seattle real dataset the effectiveness of our technique and show that it provides realistic privacy in road networks.
机译：每天都会从众多平台生成大量惊人的地理空间数据，从而触发了对个性化用户信息的需求，这正在侵蚀用户的位置隐私。为了确保道路网络上移动物体的私密性，大多数现有工程并未严格要求匿名或受干扰的地理空间点应位于路段上。因此，使结果不现实。此外，配备GPS功能的设备的人被证明是有效的传感器，可以有益于交通监控和其他众包服务。但是，出于隐私考虑，不鼓励他们参加。基于这些缺点，我们为融合隐私以在道路网络中进行地图匹配提供了理由。在本文中，我们提出了一种新颖的隐私保护地图匹配技术，该技术利用了隐马尔可夫模型，切线距离和路段的几何特性。我们的技术通过首先执行地图匹配来利用位置隐私。然后，根据一组定义的用户敏感位置，我们引入一个新的成本函数，并用它来确定我们最短路径问题中的最低成本替代专用路线。我们使用Microsoft Seattle真实数据集演示了我们技术的有效性，并表明它在道路网络中提供了现实的隐私。

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