On the Evaluation of the Privacy Breach in Disassociated Set-valued Datasets

Document Type

Article

Publication Date

2016

Subject: LCSH

Data privacy, Data sets

Disciplines

Computer Engineering | Computer Sciences | Electrical and Computer Engineering

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.

Comments

Article originally in the Proceedings of the 13th International Joint Conference on e-Business and Telecommunications - SECRYPT, 318-326, 2016 , Lisbon, Portugal.

DOI

10.5220/0005969403180326

Publisher Citation

Barakat, S.; Al Bouna, B.; Nassar, M. and Guyeux, C. (2016). On the Evaluation of the Privacy Breach in Disassociated Set-valued Datasets. In Proceedings of the 13th International Joint Conference on e-Business and Telecommunications - SECRYPT, (ICETE 2016) ISBN 978-989-758-196-0; ISSN 2184-2825, pages 318-326. DOI: 10.5220/0005969403180326

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