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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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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