Authors:
Sara Barakat
1
;
Bechara Al Bouna
1
;
Mohamed Nassar
2
and
Christophe Guyeux
3
Affiliations:
1
Antonine University, Lebanon
;
2
MUBS, Lebanon
;
3
University of Bourgogne Franche-Comté, France
Keyword(s):
Disassociation, Privacy Breach, Data Privacy, Set-valued, Privacy Preserving.
Related
Ontology
Subjects/Areas/Topics:
Data and Application Security and Privacy
;
Information and Systems Security
;
Personal Data Protection for Information Systems
;
Privacy
Abstract:
Data anonymization is gaining much attention these days as it provides the fundamental requirements to safely
outsource datasets containing identifying information. While some techniques add noise to protect privacy
others use generalization to hide the link between sensitive and non-sensitive information or separate the
dataset into clusters to gain more utility. In the latter, often referred to as bucketization, data values are kept
intact, only the link is hidden to maximize the utility. In this paper, we showcase the limits of disassociation,
a bucketization technique that divides a set-valued dataset into km-anonymous clusters. We demonstrate that
a privacy breach might occur if the disassociated dataset is subject to a cover problem. We finally evaluate the
privacy breach using the quantitative privacy breach detection algorithm on real disassociated datasets.