A User-Centric Mechanism for Sequentially Releasing Graph Datasets under Blowfish Privacy

Document Type

Article

Publication Date

2-2021

Subject: LCSH

Computer security, Data privacy

Disciplines

Computer Engineering | Computer Sciences | Electrical and Computer Engineering

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.

Comments

Article published in ACM Transactions on Internet Technology, volume 21, issue 1, 2021.

DOI

10.1145/3431501

Publisher Citation

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. https://doi.org/10.1145/3431501

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