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. © 2013 IFIP International Federation for Information Processing.
CITATION STYLE
Al Bouna, B., Clifton, C., & Malluhi, Q. (2013). Using safety constraint for transactional dataset anonymization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7964 LNCS, pp. 164–178). https://doi.org/10.1007/978-3-642-39256-6_11
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