De-anonymizing Social Networks User via Profile Similarity

@article{Wang2018DeanonymizingSN,
  title={De-anonymizing Social Networks User via Profile Similarity},
  author={Meiqi Wang and Qingfeng Tan and Xuebin Wang and Jinqiao Shi},
  journal={2018 IEEE Third International Conference on Data Science in Cyberspace (DSC)},
  year={2018},
  pages={889-895}
}
Nowadays people are likely to reveal different aspects of life on different websites. Given a user's account information on one site, we can find his or her other accounts on other websites with the help of profile matching, which can benefit multiple application domains, including recommendation, privacy, security and so on. Traditional profile matching method makes use of as many attributes as possible and calculates the similarity of attribute values one by one, which is not fit for the… CONTINUE READING

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Key Quantitative Results

  • Using the most promising similarity measure and parameters, we achieved a high reliability with a recall over 95% for a 98% precision, similar to the basic method and greatly reduced the running time.

Citations

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Correcting the Output of Approximate Graph Matching Algorithms

IEEE INFOCOM 2018 - IEEE Conference on Computer Communications • 2018
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Optimal de-anonymization in random graphs with community structure

2016 50th Asilomar Conference on Signals, Systems and Computers • 2016
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