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Using Safety Constraint for Transactional Dataset Anonymization

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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https://hal.inria.fr/hal-01490703
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Distributed under a Creative Commons Attribution 4.0 International License

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Bechara Bouna, Chris Clifton, Qutaibah Malluhi. Using Safety Constraint for Transactional Dataset Anonymization. 27th Data and Applications Security and Privacy (DBSec), Jul 2013, Newark, NJ, United States. pp.164-178, ⟨10.1007/978-3-642-39256-6_11⟩. ⟨hal-01490703⟩

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