7th International Conference on Collaborative Computing: Networking, Applications and Worksharing

Research Article

Privacy Risks and Countermeasures in Publishing and Mining Social Network Data

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  • @INPROCEEDINGS{10.4108/icst.collaboratecom.2011.247177,
        author={Chiemi Watanabe and Toshiyuki Amagasa and Ling Liu},
        title={Privacy Risks and Countermeasures in Publishing and Mining Social Network Data},
        proceedings={7th International Conference on Collaborative Computing: Networking, Applications and Worksharing},
        publisher={IEEE},
        proceedings_a={COLLABORATECOM},
        year={2012},
        month={4},
        keywords={social network privacy attack model},
        doi={10.4108/icst.collaboratecom.2011.247177}
    }
    
  • Chiemi Watanabe
    Toshiyuki Amagasa
    Ling Liu
    Year: 2012
    Privacy Risks and Countermeasures in Publishing and Mining Social Network Data
    COLLABORATECOM
    ICST
    DOI: 10.4108/icst.collaboratecom.2011.247177
Chiemi Watanabe1,*, Toshiyuki Amagasa2, Ling Liu3
  • 1: Department of Information Science, Faculty of Science Ochanomizu University
  • 2: Graduate School of Systems and Information Engineering, University of Tsukuba
  • 3: School of Computer Science, Georgia Institute of Technology
*Contact email: chiemi@is.ocha.ac.jp

Abstract

As interests in sharing and mining social network data continue to grow, we see a growing demand for privacy preserving social network data publishing. In this paper, we discuss privacy risks in publishing social network data and the design principles for developing countermeasures. The main contributions of this study are three folds. First, to the best of our knowledge, we make the first attempt to define the utility of released data in terms of exposure levels and query types, assuming queries are the most fundamental operations in social network analysis. We argue that using information exposure levels to characterize the utility of anonymized data can be used as a general and usage-neutral metric and query types can be used as the baseline usage driven utility metric. Second, we identify two types of background knowledge based inference attacks that can break some of most representative graph permutation based anonymization techniques in terms of anonymity violations. Third but not the least, we describe some design considerations for developing countermeasures in privacy preserving social network data publishing.