Abstract
Disassociation is a bucketization based anonymization technique that divides a set-valued dataset into several clusters to hide the link between individuals and their complete set of items. It increases the utility of the anonymized dataset, but on the other side, it raises many privacy concerns, one in particular, is when the items are tightly coupled to form what is called, a cover problem. In this paper, we present safe disassociation, a technique that relies on partial suppression, to overcome the aforementioned privacy breach encountered when disassociating set-valued datasets. Safe disassociation allows the km-anonymity privacy constraint to be extended to a bucketized dataset and copes with the cover problem. We describe our algorithm that achieves the safe disassociation and we provide a set of experiments to demonstrate its efficiency.
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Notes
In what follows, we use km-disassociation to denote a dataset that is disassociated and satisfies km-anonymity.
Vertical partitioning creates km-anonymous record chunks.
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Acknowledgments
This work is funded by the InMobiles companyFootnote 3 and the Labex ACTION program (contract ANR-11-LABX-01-01). Computations have been performed on the supercomputer facilities of the Mésocentre de calcul de Franche-Comté. Special thanks to Ms. Sara Barakat for her contribution in identifying the cover problem.
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Awad, N., Al Bouna, B., Couchot, JF. et al. Safe disassociation of set-valued datasets. J Intell Inf Syst 53, 547–562 (2019). https://doi.org/10.1007/s10844-019-00568-7
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DOI: https://doi.org/10.1007/s10844-019-00568-7