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引用次数: 0

摘要

在过去的十年里,联网的移动设备被广泛采用,这些设备能够通过嵌入式传感器(GPS、Wi-Fi、蓝牙、加速度计)捕捉用户的环境。移动和普适计算的出现使丰富的社交和上下文应用程序成为可能,但这些技术的使用引发了严重的隐私问题和挑战。隐私威胁来自不同的对手,从好奇的服务提供商和相同服务的其他用户到窃听者和设备上运行的好奇应用程序。可以从移动设备所有者那里收集的信息包括他们的位置、他们的社会关系和他们当前的活动。所有这些,一旦通过推理分析和组合在一起,就可以很好地说明用户的私人生活。在这次演讲中,我们将描述移动和普及网络中的隐私威胁。我们还将展示如何量化此类网络用户的隐私,并解释如何考虑有关共址的信息。我们将描述隐私增强技术(pet)可以发挥的作用,并描述其中的一些。我们还将解释如何防止应用程序在安卓系统下筛选过多的个人数据。最后,我们将提到量化自我和数字医疗带来的隐私和安全挑战
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Privacy Challenges in Mobile and Pervasive Networks
This last decade has witnessed a wide adoption of connected mobile devices able to capture the context of their owners from embedded sensors (GPS, Wi-Fi, Bluetooth, accelerometers). The advent of mobile and pervasive computing has enabled rich social and contextual applications, but the use of such technologies raises severe privacy issues and challenges. The privacy threats come from diverse adversaries, ranging from curious service providers and other users of the same service to eavesdroppers and curious applications running on the device. The information that can be collected from mobile device owners includes their locations, their social relationships, and their current activity. All of this, once analyzed and combined together through inference, can be very telling about the users' private lives. In this talk, we will describe privacy threats in mobile and pervasive networks. We will also show how to quantify the privacy of the users of such networks and explain how information on co-location can be taken into account. We will describe the role that privacy enhancing technologies (PETs) can play and describe some of them. We will also explain how to prevent apps from sifting too many personal data under Android. We will conclude by mentioning the privacy and security challenges raised by the quantified self and digital medicine
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