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International Conference on Future Data and Security Engineering

FDSE 2020: Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications pp 108–119Cite as

An Elastic Anonymization Framework for Open Data

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1306))

Abstract

Open data is a vast resource that is waiting for being utilizing. To publish data to an open environment, the privacy preserving requirement is a must. Different solutions are applied to privacy protection, and data anonymization is one of the prominent ones. This paper proposed a framework flexibly applying different anonymization models to work with an arbitrary open data management platform. An experimental setup was implemented with a heterogeneous service architecture using two datasets vary in the data volume and the number of dimensions. The measured results show that the proposed method produces anonymized data in an acceptable time using different anonymization techniques and settings, giving high quality outputs.

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Notes

  1. 1.

    https://ckan.org/.

  2. 2.

    https://data.gov.uk/.

  3. 3.

    https://data.overheid.nl.

  4. 4.

    https://www.data.gov.

  5. 5.

    https://duraspace.org/dspace/.

  6. 6.

    https://openknowledge.worldbank.org/.

  7. 7.

    https://www.repository.cam.ac.uk/.

  8. 8.

    https://dspace.mit.edu/.

  9. 9.

    https://www.rabbitmq.com/.

  10. 10.

    https://github.com/letrunghieu/transformation-benchmark/tree/master/data.

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Acknowledgements

This work is supported by a project with the Department of Science and Technology, Ho Chi Minh City, Vietnam (contract with HCMUT No. 08/2018/HĐQKHCN, dated 16/11/2018). We also thank all members of AC Lab and D-STAR Lab for their great supports and comments during the preparation of this paper.

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Authors and Affiliations

  1. Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam

    Trung Hieu Le & Tran Khanh Dang

  2. Vietnam National University Ho Chi Minh City (VNU-HCM), Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Vietnam

    Trung Hieu Le & Tran Khanh Dang

Authors
  1. Trung Hieu Le
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  2. Tran Khanh Dang
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Correspondence to Tran Khanh Dang .

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Editors and Affiliations

  1. Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam

    Tran Khanh Dang

  2. Johannes Kepler University of Linz, Linz, Austria

    Josef Küng

  3. Hosei University, Tokyo, Japan

    Makoto Takizawa

  4. Sungkyunkwan University, Suwon, Korea (Republic of)

    Tai M. Chung

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Le, T.H., Dang, T.K. (2020). An Elastic Anonymization Framework for Open Data. In: Dang, T.K., Küng, J., Takizawa, M., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2020. Communications in Computer and Information Science, vol 1306. Springer, Singapore. https://doi.org/10.1007/978-981-33-4370-2_8

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  • DOI: https://doi.org/10.1007/978-981-33-4370-2_8

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  • Online ISBN: 978-981-33-4370-2

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