It's Too Noisy in Here: Using Projection to Improve Differential Privacy on RDF Graphs

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Sara Taki, Cédric Eichler, Benjamin Nguyen

Published: 2022, Last Modified: 13 Mar 2026ADBIS (Short Papers) 2022EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Differential privacy is one of the most popular and prevalent definitions of privacy, providing a robust and mathematically rigid definition of privacy. In the last decade, adaptation of DP to graph data has received growing attention. Most efforts have been dedicated to unlabeled homogeneous graphs, while labeled graphs with an underlying semantic (e.g. RDF) have been mildly addressed.