sis 13(1): e3

Research Article

Privacy Preserving Large-Scale Rating Data Publishing

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  • @ARTICLE{10.4108/trans.sis.2013.01-03.e3,
        author={Xiaoxun Sun and Lili Sun},
        title={Privacy Preserving Large-Scale Rating Data Publishing},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={1},
        number={1},
        publisher={ICST},
        journal_a={SIS},
        year={2013},
        month={2},
        keywords={Privacy preserving, anonymity},
        doi={10.4108/trans.sis.2013.01-03.e3}
    }
    
  • Xiaoxun Sun
    Lili Sun
    Year: 2013
    Privacy Preserving Large-Scale Rating Data Publishing
    SIS
    ICST
    DOI: 10.4108/trans.sis.2013.01-03.e3
Xiaoxun Sun1, Lili Sun2
  • 1: Australian Council for Educational Research, Australia
  • 2: Department of Mathematics & Computing, University of Southern Queensland, Australia

Abstract

Large scale rating data usually contains both ratings of sensitive and non-sensitive issues, and the ratings of sensitive issues belong to personal privacy. Even when survey participants do not reveal any of their ratings, their survey records are potentially identifiable by using information from other public sources. In order to protect the privacy in the large-scale rating data, it is important to propose new privacy principles which consider the properties of the rating data. Moreover, given the privacy principle, how to efficiently determine whether the rating data satisfied the required privacy principle is crucial as well. Furthermore, if the privacy principle is not satisfied, an efficient method is needed to securely publish the large-scale rating data. In this paper, all these problem will be addressed.