8th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing

Research Article

Privacy-Preserving Assessment of Social Network Data Trustworthiness

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  • @INPROCEEDINGS{10.4108/icst.collaboratecom.2012.250417,
        author={Chenyun Dai and Fang-Yu Rao and Traian Truta and Elisa Bertino},
        title={Privacy-Preserving Assessment of Social Network Data Trustworthiness},
        proceedings={8th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing},
        publisher={IEEE},
        proceedings_a={COLLABORATECOM},
        year={2012},
        month={12},
        keywords={social network privacy trustworthiness},
        doi={10.4108/icst.collaboratecom.2012.250417}
    }
    
  • Chenyun Dai
    Fang-Yu Rao
    Traian Truta
    Elisa Bertino
    Year: 2012
    Privacy-Preserving Assessment of Social Network Data Trustworthiness
    COLLABORATECOM
    ICST
    DOI: 10.4108/icst.collaboratecom.2012.250417
Chenyun Dai1,*, Fang-Yu Rao1, Traian Truta2, Elisa Bertino1
  • 1: Purdue University
  • 2: Northern Kentucky University
*Contact email: daic@cs.purdue.edu

Abstract

Extracting useful knowledge from social network datasets is a challenging problem. To add to the difficulty of this problem, privacy concerns that exist for many social network datasets have restricted the ability to analyze these networks and consequently to maximize the knowledge that can be extracted from them. This paper addresses this issue by introducing the problem of data trustworthiness in social networks when repositories of anonymized social networks exist that can be used to assess such trustworthiness. Three trust score computation models (absolute, relative, and weighted) that can be instantiated for specific anonymization models are defined and algorithms to calculate these trust scores are developed. Using both real and synthetic social networks, the usefulness of the trust score computation is validated through a series of experiments.