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

Tekli, Jimmy; Al Bouna, Bechara; Issa, Youssef Bou; Kamradt, Marc; Haraty, Ramzi

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

Tekli, Jimmy; Al Bouna, Bechara; Issa, Youssef Bou; Kamradt, Marc; Haraty, Ramzi

URI: http://hdl.handle.net/10725/10229

URL: https://link.springer.com/chapter/10.1007/978-3-319-99807-7_35

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

Date: 2019-03-18

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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.

Citation:

Tekli, J., Al Bouna, B., Issa, Y. B., Kamradt, M., & Haraty, R. (2018, September). (k, l)-Clustering for Transactional Data Streams Anonymization. In International Conference on Information Security Practice and Experience (pp. 544-556). Springer, Cham.

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