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Privacy versus Location Accuracy in Opportunistic Wearable Networks

机译:机会可穿戴网络中的隐私与位置准确性

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Future wearable devices are expected to increasingly exchange their positioning information with various Location-Based Services (LBSs). Wearable applications can include activity-based health and fitness recommendations, location-based social networking, location-based gamification, among many others. With the growing opportunities for LBSs, it is expected that location privacy concerns will also increase significantly. Particularly, in opportunistic wireless networks based on device-to-device (D2D) connectivity, a user can request a higher level of control over own location privacy, which may result in more flexible permissions granted to wearable devices. This translates into the ability to perform location obfuscation to the desired degree when interacting with other wearables or service providers across the network. In this paper, we argue that specific errors in the disclosed location information feature two components: a measurement error inherent to the localization algorithm used by a wearable device and an intentional (or obfuscation) error that may be based on a trade-off between a particular LBS and a desired location privacy level. This work aims to study the trade-off between positioning accuracy and location information privacy in densely crowded scenarios by introducing two privacy-centric metrics.
机译:预计未来的可穿戴设备将越来越多地与各种基于位置的服务(LBS)交换其定位信息。可穿戴应用程序可以包括基于活动的健康和健身建议,基于位置的社交网络,基于位置的游戏化等。随着LBS机会的增长,预计对位置隐私的关注也将大大增加。特别地,在基于设备到设备(D2D)连接的机会性无线网络中,用户可以请求对自己的位置隐私进行更高级别的控制,这可能导致授予可穿戴设备更灵活的权限。这意味着在与网络上的其他可穿戴设备或服务提供商进行交互时,可以执行位置混淆处理到所需程度的功能。在本文中,我们认为所公开的位置信息中的特定错误具有两个组成部分:可穿戴设备使用的定位算法固有的测量错误,以及可能基于两者之间的折衷而产生的故意(或混淆)错误。特定的LBS和所需的位置隐私级别。这项工作旨在通过引入两个以隐私为中心的指标来研究在拥挤的场景中定位精度和位置信息隐私之间的权衡。

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