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  • 3LP: Three Layers of Protection for Individual Privacy in Facebook

    Author(s)
    Reza, Khondker Jahid
    Islam, Md Zahidul
    Estivill-Castro, Vladimir
    Griffith University Author(s)
    Estivill-Castro, Vladimir
    Year published
    2017
    Metadata
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    Abstract
    The possibility that an unauthorised agent is able to infer a user’s hidden information (an attribute’s value) is known as attribute inference risk. It is one of the privacy issues for Facebook users in recent times. An existing technique [1] provides privacy by suppressing users’ attribute values from their profiles. However, suppression of an attribute value sometimes is not enough to secure a user’s confidential information. In this paper, we experimentally demonstrate that (after taking necessary steps on attribute values) a user’s sensitive information can still be inferred through his/her friendship information. We ...
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    The possibility that an unauthorised agent is able to infer a user’s hidden information (an attribute’s value) is known as attribute inference risk. It is one of the privacy issues for Facebook users in recent times. An existing technique [1] provides privacy by suppressing users’ attribute values from their profiles. However, suppression of an attribute value sometimes is not enough to secure a user’s confidential information. In this paper, we experimentally demonstrate that (after taking necessary steps on attribute values) a user’s sensitive information can still be inferred through his/her friendship information. We evaluated our approach experimentally on two datasets. We propose 3LP, a new three layers protection technique, to provide privacy protection to users of on-line social networks.
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    Journal Title
    IFIP Advances in Information and Communication Technology
    Volume
    502
    DOI
    https://doi.org/10.1007/978-3-319-58469-0_8
    Copyright Statement
    Copyright IFIP, 2017. This is the author's version of the work. It is posted here by permission of IFIP for your personal use. Not for redistribution. The definitive version was published in IFIP Advances in Information and Communication Technology, 502, 2017(Boston: Springer), 108-123.
    Subject
    Pattern recognition
    Data mining and knowledge discovery
    Publication URI
    http://hdl.handle.net/10072/355440
    Collection
    • Journal articles

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