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Ontology-Based Approach for the Measurement of Privacy Disclosure

Author

Listed:
  • Nafei Zhu

    (Beijing University of Technology)

  • Baocun Chen

    (Beijing University of Technology)

  • Siyu Wang

    (Beijing University of Technology)

  • Da Teng

    (Beijing University of Technology)

  • Jingsha He

    (Beijing University of Technology)

Abstract

Privacy protection has received a lot of attention in recent years since in the era of big data, abundant information about individuals can be easily acquired. Meanwhile, as a prerequisite for effective privacy protection, the measurement of privacy disclosure is essential. Although some work has been done on the evaluation of privacy disclosure via quantification for the protection of privacy, not much attention has been placed on exploring the relationships between privacy information, resulting in underestimation, if not ill-formed reasoning, of privacy disclosure. In this paper, we propose an ontology-based approach to measure privacy disclosure by exploring the relationships between privacy information based on the WordNet. We first propose an algorithm for deriving or measuring privacy disclosure based on a set of words or concepts from text data related to individuals to ensure that the disclosure of certain user privacy can still be deduced and measured even if the set of words or concepts don’t seem to be much related to it. We then perform a set of experiment by applying the proposed algorithm to some public information of ten public figures from different walks of life to evaluate the effectiveness of the algorithm and to shed some light on the characteristics of privacy disclosure in the real world in the era of big data. The work can thus serve as the foundation for the development of mechanisms for limiting or reducing privacy disclosure to achieve better protection of individual privacy.

Suggested Citation

  • Nafei Zhu & Baocun Chen & Siyu Wang & Da Teng & Jingsha He, 2022. "Ontology-Based Approach for the Measurement of Privacy Disclosure," Information Systems Frontiers, Springer, vol. 24(5), pages 1689-1707, October.
    • Handle: RePEc:spr:infosf:v:24:y:2022:i:5:d:10.1007_s10796-021-10180-2
    DOI: 10.1007/s10796-021-10180-2
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    References listed on IDEAS

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    1. Jyoti Choudrie & Shruti Patil & Ketan Kotecha & Nikhil Matta & Ilias Pappas, 2021. "Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study," Information Systems Frontiers, Springer, vol. 23(6), pages 1431-1465, December.
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