What is it about?
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.
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Why is it important?
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).
Perspectives
Writing this article was a great pleasure as it has co-authors with whom I have had long standing collaborations.
Dr. Ramzi A. Haraty
Lebanese American University
Read the Original
This page is a summary of: A User-Centric Mechanism for Sequentially Releasing Graph Datasets under Blowfish Privacy, ACM Transactions on Internet Technology, February 2021, ACM (Association for Computing Machinery),
DOI: 10.1145/3431501.
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