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Cover for Public Information Exposure Detection: Helping Users Understand Their Web Footprints
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dc.date.accessioned2015-09-17T12:59:20Zen
dc.date.available2015-09-17T12:59:20Zen
dc.date.created2015en
dc.date.issueden
dc.identifier.otherAPT-BAG: georgetown.edu.10822_761519.tar;APT-ETAG: 1c0bbc63dbde2e8b76c16721c7ee0d5fen
dc.identifier.otherAPT-BAG: georgetown.edu.10822_761519.tar;APT-ETAG: a8f1aa3375a995e9f6d60a8efeceb6c0en
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dc.description.abstractTo help users better understand the potential risks associated with publishing data publicly, as well as the quantity and sensitivity of information that can be obtained by combining data from various online sources, we introduce a novel information exposure detection framework that generates and analyzes the web footprints users leave across the social web. Web footprints are the traces of one’s online social activities represented by a set of attributes that are known or can be inferred with a high probability by an adversary who has basic information about a user from his/her public profiles. Our framework employs new probabilistic operators, novel pattern-based attribute extraction from text, and a population-based inference engine to generate web footprints. Using a web footprint, the framework then quantifies a user’s level of information exposure relative to others with similar traits, as well as with regard to others in the population. Evaluation over public profiles from multiple sites (Google+, LinkeIn, FourSquare, and Twitter) shows that the proposed framework effectively detects and quantifies information exposure using a small amount of initial knowledge.en
dc.languageEnglishen
dc.sourceInternational Conference on Advances in Social Networks Analysis and Miningen
dc.subjectComputer Scienceen
dc.subjectWeb footprintsen
dc.subject.lcshData protectionen
dc.titlePublic Information Exposure Detection: Helping Users Understand Their Web Footprintsen
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