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(k, l)-Clustering for Transactional Data Streams Anonymization

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Information Security Practice and Experience (ISPEC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11125))

Abstract

In this paper, we address the correlation problem in the anonymization of transactional data streams. We propose a bucketization-based technique, entitled (k, l)-clustering to prevent such privacy breaches by ensuring that the same k individuals remain grouped together over the entire anonymized stream. We evaluate our algorithm in terms of utility by considering two different (k, l)-clustering approaches.

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Notes

  1. 1.

    https://github.com/JMTCoder/test12/blob/master/sourcedata.txt

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Author information

Authors and Affiliations

  1. BMW Group, Munich, Germany

    Jimmy Tekli & Marc Kamradt

  2. TICKET Lab., Antonine University, Baabda, Lebanon

    Bechara Al Bouna & Youssef Bou Issa

  3. Université de Franche Comté, Belfort, France

    Jimmy Tekli

  4. Department of Computer Science and Mathematics, Lebanese American Univesity, Beirut, Lebanon

    Ramzi Haraty

Authors
  1. Jimmy Tekli
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  2. Bechara Al Bouna
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  3. Youssef Bou Issa
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  4. Marc Kamradt
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  5. Ramzi Haraty
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Corresponding authors

Correspondence to Jimmy Tekli , Bechara Al Bouna or Youssef Bou Issa .

Editor information

Editors and Affiliations

  1. University of Aizu, Aizuwakamatsu, Japan

    Chunhua Su

  2. Meiji University, Tokyo, Japan

    Hiroaki Kikuchi

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Tekli, J., Al Bouna, B., Bou Issa, Y., Kamradt, M., Haraty, R. (2018). (k, l)-Clustering for Transactional Data Streams Anonymization. In: Su, C., Kikuchi, H. (eds) Information Security Practice and Experience. ISPEC 2018. Lecture Notes in Computer Science(), vol 11125. Springer, Cham. https://doi.org/10.1007/978-3-319-99807-7_35

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  • DOI: https://doi.org/10.1007/978-3-319-99807-7_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99806-0

  • Online ISBN: 978-3-319-99807-7

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