覆盖位置:利用基于位置的服务而不透露位置

Sai Teja Peddinti, Avis Dsouza, Nitesh Saxena
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引用次数: 28

摘要

基于位置的服务(lbs)越来越受欢迎,这是由于开发了大量有趣且重要的应用程序。然而,使用这些服务的用户担心他们的位置隐私,因为他们被迫向不受信任的第三方透露他们的敏感位置信息。在本文中,我们提出了一种新的隐私保护方法,即覆盖位置,该方法允许用户在不暴露其实际位置的情况下访问LBS。根据其当前位置,用户的设备查询几个特定选择的周围位置,并从每个查询位置获得的结果中构造与其位置对应的结果。由于用户位置不会离开用户的设备——无论是作为纬度和经度对,还是作为模糊区域——用户的隐私得到了非常高的保证。覆盖位置方法只需要对用户的设备进行最小的更改,并且可以由注重隐私的用户轻松部署。试图识别用户位置的攻击者只能将位置解析为几个三角形区域,而不能解析为实际位置本身。我们根据查询的位置数量和解决的三角形区域下的总面积来评估Cover Locations提供的隐私。当攻击者可以使用机器学习技术访问短期用户历史时,我们还确定了掩护位置方法的鲁棒性。总体而言,我们的结果表明,所提出的解决方案需要少量的计算,而不需要任何带外信息,如区域内的交通密度或路网信息,优于其他基于客户端的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cover locations: availing location-based services without revealing the location
Location-Based Services (LBSs) have been gaining popularity due to a wide range of interesting and important applications being developed. However, the users availing such services are concerned about their location privacy, in that they are forced to reveal their sensitive location information to untrusted third-parties. In this paper, we propose a new privacy-preserving approach, Cover Locations, which allows a user to access an LBS without revealing his/her actual location. Based on its current location, the user's device queries for a few specifically chosen surrounding locations and constructs the results corresponding to its location from the results obtained for each queried location. Since the user location does not leave the user's device - as either a latitude and longitude pair, or as an obfuscated region - the user is guaranteed very high level of privacy. The Cover Locations approach only requires minimal changes on the user's device and can be readily deployed by privacy-conscious users. An adversary, trying to identify the user location, can only resolve the location to few triangular regions and not to the actual location itself. We evaluate the privacy provided by Cover Locations based on the number of locations queried and the total area under the resolved triangular regions. We also ascertain the robustness of Cover Locations approach when the adversary has access to a short-term user history, employing machine learning techniques. Overall, our results show that the proposed solution, which requires minor computations without the need for any out-of-band information such as traffic densities in a region or the road network information, is superior to other client-based solutions.
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