• DocumentCode
    1431734
  • Title

    Modeling Unintended Personal-Information Leakage from Multiple Online Social Networks

  • Author

    Irani, Danesh ; Webb, Steve ; Pu, Calton ; Li, Kang

  • Author_Institution
    Georgia Inst. of Technol., Atlanta, GA, USA
  • Volume
    15
  • Issue
    3
  • fYear
    2011
  • Firstpage
    13
  • Lastpage
    19
  • Abstract
    Most people have multiple accounts on different social networks. Because these networks offer various levels of privacy protection, the weakest privacy policies in the social network ecosystem determine how much personal information is disclosed online. A new information leakage measure quantifies the information available about a given user. Using this measure makes it possible to evaluate the vulnerability of a user´s social footprint to two known attacks: physical identification and password recovery. Experiments show the measure´s usefulness in quantifying information leakage from publicly crawled information and also suggest ways of better protecting privacy and reducing information leakage in the social Web.
  • Keywords
    data privacy; social networking (online); multiple online social networks; privacy protection; publicly crawled information; social Web; unintended personal information leakage; user social footprint; Aggregates; Authentication; Electronic mail; Modeling; Online services; Privacy; Social network services; Social networks; personal information leakage; security and privacy;
  • fLanguage
    English
  • Journal_Title
    Internet Computing, IEEE
  • Publisher
    ieee
  • ISSN
    1089-7801
  • Type

    jour

  • DOI
    10.1109/MIC.2011.25
  • Filename
    5696719