An optimal \((\epsilon, \delta )\)-differentially private learning of distributed deep fuzzy models

DOI10.1016/j.ins.2020.07.044zbMath1475.68120OpenAlexW3048427923MaRDI QIDQ2055528

Bernhard A. Moser, Mohit Kumar, Bernhard Freudenthaler, Michael Rossbory

Publication date: 1 December 2021

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2020.07.044

zbMATH Keywords

differential privacyvariational Bayesfuzzy machine learning

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Bayesian inference (62F15) Learning and adaptive systems in artificial intelligence (68T05) Privacy of data (68P27)

Related Items (1)

Pricing GAN-based data generators under Rényi differential privacy

Uses Software

• Caltech-256

Cites Work

• A simplified implementation of hierarchical fuzzy systems
• The Optimal Noise-Adding Mechanism in Differential Privacy
• Optimal Noise Adding Mechanisms for Approximate Differential Privacy
• The Algorithmic Foundations of Differential Privacy
• Our Data, Ourselves: Privacy Via Distributed Noise Generation
• Universally Utility-maximizing Privacy Mechanisms
• Variational Bayes In Private Settings (VIPS)
• Theory of Cryptography

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