• DocumentCode
    3717345
  • Title

    Toward big data risk analysis

  • Author

    Ernesto Damiani

  • Author_Institution
    Etisalat British Telecom Innovation Center, Khalifa University of Science, Technology and Research, Abu Dhabi, UAE
  • fYear
    2015
  • Firstpage
    1905
  • Lastpage
    1909
  • Abstract
    The advent of social networks and Internet-of-Things has resulted in unprecedented capability of collecting, sharing and analyzing massive amounts of data. From a security perspective, Big Data may seriously weaken confidentiality, as techniques for improving Big Data analytics performance-including early fusion of heterogeneous data sources - increase the hidden redundancy of data representation, generating ill-protected copies. This gray area of redundancy triggers new disclosure threats that challenge traditional techniques to protect privacy and confidentiality. This position paper starts by proposing a definition of the Big Data Leak threat (as opposed to the one of data breach) and its role as a component of disclosure risk. Then, it discusses how a paradigm of Known, Detect, Contain and Recover could be used to establish Big Data security practices for containing disclosure risks connected to Big Data analytics.
  • Keywords
    "Big data","Data models","Redundancy","Security","Companies","ISO Standards","Taxonomy"
  • Publisher
    ieee
  • Conference_Titel
    Big Data (Big Data), 2015 IEEE International Conference on
  • Type

    conf

  • DOI
    10.1109/BigData.2015.7363966
  • Filename
    7363966