University of Oulu

Elie Chicha, Bechara Al Bouna, Mohamed Nassar, Richard Chbeir, Ramzi A. Haraty, Mourad Oussalah, Djamal Benslimane, and Mansour Naser Alraja. 2021. A User-Centric Mechanism for Sequentially Releasing Graph Datasets under Blowfish Privacy. ACM Trans. Internet Technol. 21, 1, Article 20 (February 2021), 25 pages. DOI:https://doi.org/10.1145/3431501

A user-centric mechanism for sequentially releasing graph datasets under blowfish privacy

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Author: Chicha, Elie1,2; Al Bouna, Bechara1; Nassar, Mohamed3;
Organizations: 1TICKET Lab., Antonine University, Hadat-Baabda, Lebanon
2LIUPPA Lab., University of Pau & Pays Adour, Anglet, France
3Computer Science Department, American University of Beirut, Lebanon
4Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon
5Center for Ubiquitous Computing, Faculty of Information Technology and Electrical Engineering, University of Oulu, FI90014, Finland
6Université Claude Bernard Lyon 1, LIRIS, Villeurbanne, France
7Department of Management Information Systems, College of Commerce and Business Administration, Dhofar University, Oman
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 2.8 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2021051730031
Language: English
Published: Association for Computing Machinery, 2021
Publish Date: 2021-05-17
Description:

Abstract

In this article, we present a privacy-preserving technique for user-centric multi-release graphs. Our technique consists of sequentially releasing anonymized versions of these graphs under Blowfish Privacy. To do so, we introduce a graph model that is augmented with a time dimension and sampled at discrete time steps. We show that the direct application of state-of-the-art privacy-preserving Differential Private techniques is weak against background knowledge attacker models. We present different scenarios where randomizing separate releases independently is vulnerable to correlation attacks. Our method is inspired by Differential Privacy (DP) and its extension Blowfish Privacy (BP). To validate it, we show its effectiveness as well as its utility by experimental simulations.

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Series: ACM transactions on internet technology
ISSN: 1533-5399
ISSN-E: 1557-6051
ISSN-L: 1533-5399
Volume: 21
Issue: 1
Article number: 20
DOI: 10.1145/3431501
OADOI: https://oadoi.org/10.1145/3431501
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Subjects:
Copyright information: © 2021 Association for Computing Machinery. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on Internet Technology, https://doi.org/10.1145/3431501.