Fingerprinting Mobile Devices Using Personalized Configurations
Fingerprinting Mobile Devices Using Personalized Configurations
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DOI：
10.1515/popets-2015-0027
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发表时间：
2016
影响因子：
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通讯作者：
Andreas Kurtz;Hugo Gascon;Tobias Becker;Konrad Rieck;F. Freiling
中科院分区：
文献类型：
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作者：
Andreas Kurtz;Hugo Gascon;Tobias Becker;Konrad Rieck;F. Freiling
最近，苹果删除了对各种设备硬件标识符的访问，这些标识符经常被iOS第三方应用程序滥用来跟踪用户。因此，我们现在正在研究智能手机用户在多大程度上仍然可以通过他们的个性化设备配置来唯一识别。以苹果的iOS为例，我们展示了如何使用29种不同的配置功能来计算设备指纹。这些功能可以通过官方SDK从任意第三方应用中查询。基于来自大约8，000个不同的真实世界设备的近13，000个指纹的实验评估表明：（1）所有指纹都是唯一和可区分的;（2）利用监督学习方法，随着时间的推移，可以以97%的总准确率识别返回用户或其设备
Abstract Recently, Apple removed access to various device hardware identifiers that were frequently misused by iOS third-party apps to track users. We are, therefore, now studying the extent to which users of smartphones can still be uniquely identified simply through their personalized device configurations. Using Apple’s iOS as an example, we show how a device fingerprint can be computed using 29 different configuration features. These features can be queried from arbitrary thirdparty apps via the official SDK. Experimental evaluations based on almost 13,000 fingerprints from approximately 8,000 different real-world devices show that (1) all fingerprints are unique and distinguishable; and (2) utilizing a supervised learning approach allows returning users or their devices to be recognized with a total accuracy of 97% over time