Public Information Exposure Detection: Helping Users Understand Their Web Footprints
Yang, Grace Hui
To 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.
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