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Privacy-preserving data mining in peer to peer networks

Hussain, I, Irakleous, M, Siddiqi, MA and Saraee, M 2010, 'Privacy-preserving data mining in peer to peer networks' , in: proceedings from the Annual International Conference on Data Analysis, Data Quality and Metadata Management , GSTF.

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    Abstract

    In recent years, privacy-preserving data mining has been studied extensively, due to the wide increase of sensitive information on the internet. A number of algorithms and procedures have been designed, some of which are yet to be implemented, but a few of them are actually employed in the form of software systems to preserve the privacy of users, and the content in peer-to-peer networks. Privacy issues are becoming widely recognized when using peer-to-peer networks. In this paper, we provide a review of the privacy-preserving data mining techniques used in order to overcome privacy issues. We discuss methods of sanitization, data distortion, data hiding, cryptography and the data mining algorithm KDEC. Further discussion involves data transfer using proxy techniques, creating social communities among peer-to-peer users forming trusted peers. These techniques have shown to administer the issue of preserving data however show lack of scalability and performance. We design a framework to perform a comparison study on the techniques shown above and present the results with some recommendations of how we think the issues could be unraveled.

    Item Type: Book Section
    Themes: Media, Digital Technology and the Creative Economy
    Schools: Colleges and Schools > College of Science & Technology > School of Computing, Science and Engineering > Data Mining and Pattern Recognition Research Centre
    Publisher: GSTF
    Refereed: Yes
    Related URLs:
    Funders: Non funded research
    Depositing User: Dr Mo Saraee
    Date Deposited: 27 Oct 2011 11:26
    Last Modified: 20 Aug 2013 18:16
    URI: http://usir.salford.ac.uk/id/eprint/18717

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