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Understanding Internet access trends at a global scale, i.e., how people use the Internet, is a challenging problem that is typically addressed by analyzing network traces. However, obtaining such traces presents its own set of challenges owing to either privacy concerns or to other operational difficulties. The key hypothesis of the authors' work here is that most of the information needed to profile the Internet endpoints is already available around users - on the web. In this paper, they introduce a novel approach for profiling and classifying endpoints. They implement and deploy a Google-based profiling tool, that accurately characterizes endpoint behavior by collecting and strategically combining information freely available on the web.
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