Feeling-based location privacy protection for location-based services
Feeling-based location privacy protection for location-based services
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DOI：
10.1145/1653662.1653704
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发表时间：
2009-11
期刊：
影响因子：
--
通讯作者：
Toby Xu;Ying Cai
中科院分区：
文献类型：
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作者：
Toby Xu;Ying Cai
匿名位置信息可以与诸如家庭和办公室之类的受限空间相关联，以用于主体重新识别。这使得为基于位置的服务的用户提供位置隐私保护成为一个巨大的挑战。现有的工作采用传统的K-匿名模型，并确保服务请求中公开的每个位置是一个空间区域，已被至少K个用户访问。该策略要求用户指定适当的K值，以实现期望的隐私保护水平。这是有问题的，因为隐私是关于感觉的，一个人用数字来衡量她的感觉是很尴尬的。在本文中，我们提出了一个基于情感的隐私模型。该模型允许用户通过指定一个公共区域来表达她的隐私要求，如果该区域被报告为她的位置，用户会感到舒适。公共区域的受欢迎程度，使用基于其访问者在其中的足迹的熵来测量，然后被用作用户期望的隐私保护水平。有了这个模型，我们提出了一种新的技术，允许用户的位置信息被报告尽可能准确，同时提供她足够的位置隐私保护。新技术支持轨迹伪装，可用于用户需要沿着无法预测的轨迹进行频繁位置更新的应用场景。除了评估所提出的技术的有效性，在各种条件下，通过模拟，我们还实现了一个实验系统的位置隐私感知使用基于位置的服务。
Anonymous location information may be correlated with restricted spaces such as home and office for subject re-identification. This makes it a great challenge to provide location privacy protection for users of location-based services. Existing work adopts traditional K-anonymity model and ensures that each location disclosed in service requests is a spatial region that has been visited by at least K users. This strategy requires a user to specify an appropriate value of K in order to achieve a desired level of privacy protection. This is problematic because privacy is about feeling, and it is awkward for one to scale her feeling using a number. In this paper, we propose a feeling-based privacy model. The model allows a user to express her privacy requirement by specifying a public region, which the user would feel comfortable if the region is reported as her location. The popularity of the public region, measured using entropy based on its visitors' footprints inside it, is then used as the user's desired level of privacy protection. With this model in place, we present a novel technique that allows a user's location information to be reported as accurate as possible while providing her sufficient location privacy protection. The new technique supports trajectory cloaking and can be used in application scenarios where a user needs to make frequent location updates along a trajectory that cannot be predicted. In addition to evaluating the effectiveness of the proposed technique under various conditions through simulation, we have also implemented an experimental system for location privacy-aware uses of location-based services.