Advanced Hybrid Information Processing. First International Conference, ADHIP 2017, Harbin, China, July 17–18, 2017, Proceedings

Research Article

A Quantitative Model for Analysis and Evaluation of Tor Hidden Service Discovery

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  • @INPROCEEDINGS{10.1007/978-3-319-73317-3_10,
        author={Peipeng Liu and Xiao Wang and Xin He and Chenglong Li and Shoufeng Cao and Longtao He and Jiawei Zhu},
        title={A Quantitative Model for Analysis and Evaluation of Tor Hidden Service Discovery},
        proceedings={Advanced Hybrid Information Processing. First International Conference, ADHIP 2017, Harbin, China, July 17--18, 2017, Proceedings},
        proceedings_a={ADHIP},
        year={2018},
        month={2},
        keywords={Tor Hidden service Discovery Coupon collector},
        doi={10.1007/978-3-319-73317-3_10}
    }
    
  • Peipeng Liu
    Xiao Wang
    Xin He
    Chenglong Li
    Shoufeng Cao
    Longtao He
    Jiawei Zhu
    Year: 2018
    A Quantitative Model for Analysis and Evaluation of Tor Hidden Service Discovery
    ADHIP
    Springer
    DOI: 10.1007/978-3-319-73317-3_10
Peipeng Liu1, Xiao Wang1, Xin He1, Chenglong Li1, Shoufeng Cao1, Longtao He1, Jiawei Zhu1,*
  • 1: National Computer Network Emergency Response Technical Team/Coordination Center
*Contact email: zhujw.happy@163.com

Abstract

Tor is one of the most popular anonymous communication systems, and its ability of providing receiver anonymity makes more and more attractive. However, with the exposure of illegal contents such as child pornography and drug trades in hidden services, it becomes urgent to make a comprehensive analysis and evaluation of hidden services in the Tor network. In this paper, based on the frequent updates of hidden service descriptors, we proposed an approach to model Tor hidden service discovery as a generalized coupon collector problem with group drawings. Our experiments based on the real Tor network proved the efficiency and feasibility of the proposed model, which proved the possibility of harvesting most of hidden services with a small amount of resources.