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

  • Jimmy Tekli
  • Bechara Al Bouna
  • Youssef Bou Issa
  • Marc Kamradt
  • Ramzi Haraty
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Data privacy Data stream Correlation Anonymization 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.BMW GroupMunichGermany
  2. 2.TICKET Lab.Antonine UniversityBaabdaLebanon
  3. 3.Université de Franche ComtéBelfortFrance
  4. 4.Department of Computer Science and MathematicsLebanese American UnivesityBeirutLebanon

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