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Conference Papers Year : 2023

Can sparsity improve the privacy of neural networks?


Sparse neural networks are mainly motivated by ressource efficiency since they use fewer parameters than their dense counterparts but still reach comparable accuracies. This article empirically investigates whether sparsity could also improve the privacy of the data used to train the networks. The experiments show positive correlations between the sparsity of the model, its privacy, and its classification error. Simply comparing the privacy of two models with different sparsity levels can yield misleading conclusions on the role of sparsity, because of the additional correlation with the classification error. From this perspective, some caveats are raised about previous works that investigate sparsity and privacy.
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Origin : Files produced by the author(s)
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Dates and versions

hal-04062317 , version 1 (07-04-2023)
hal-04062317 , version 2 (18-04-2023)
hal-04062317 , version 3 (12-10-2023)



Antoine Gonon, Léon Zheng, Clément Lalanne, Quoc-Tung Le, Guillaume Lauga, et al.. Can sparsity improve the privacy of neural networks?. GRETSI 2023 - XXIXème Colloque Francophone de Traitement du Signal et des Images, Aug 2023, Grenoble, France. ⟨hal-04062317v3⟩
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