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Triangle randomization for social network data anonymization
Brankovic, Ljiljana; López Lorenzo, Ignacio; Miller, Mirka; Sebé Feixas, Francesc
In order to protect privacy of social network participants, network graph data should be anonymised prior to its release. Most proposals in the literature aim to achieve $k$-anonymity under specific assumptions about the background information available to the attacker. Our method is based on randomizing the location of the triangles in the graph. We show that this simple method preserves the main structural parameters of the graph to a high extent, while providing a high re-identification confusion.
Social network
Xarxes socials
Social networks
cc-by (c) Society of Mathematicians, Physicists and Astronomers of Slovenia, Institute of Mathematics, Physics, and Mechanics, University of Primorska (Slovenia) 2014
Society of Mathematicians, Physicists and Astronomers of Slovenia;
Institute of Mathematics, Physics, and Mechanics;
University of Primorska (Slovenia)

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