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Safe disassociation of set-valued datasets

  • Nancy AwadEmail author
  • Bechara Al Bouna
  • Jean-Francois Couchot
  • Laurent Philippe
Article
  • 33 Downloads

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.

Keywords

Disassociation Cover problem Data privacy Set-valued Privacy preserving 

Notes

Acknowledgments

This work is funded by the InMobiles company3 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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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.TICKET LaboratoryAntonine UniversityHadat-BaabdaLebanon
  2. 2.FEMTO-ST Institute, UMR 6174 CNRSUniversité of BourgogneFranche-ComtéFrance

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