Abstract
In this paper, we address privacy breaches in transactional data where individuals have multiple tuples in a dataset. We provide a safe grouping principle to ensure that correlated values are grouped together in unique partitions that enforce l-diversity at the level of individuals. We conduct a set of experiments to evaluate privacy breach and the anonymization cost of safe grouping.
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Al Bouna, B., Clifton, C., Malluhi, Q. (2013). Using Safety Constraint for Transactional Dataset Anonymization. In: Wang, L., Shafiq, B. (eds) Data and Applications Security and Privacy XXVII. DBSec 2013. Lecture Notes in Computer Science, vol 7964. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39256-6_11
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DOI: https://doi.org/10.1007/978-3-642-39256-6_11
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