Cited by 212Privacy-Preserving Technologies in DataCryptography and Data SecurityCloud Data Security Solutions

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A review of privacy-preserving techniques for deep learning

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Boulemtafes, Amine, Derhab, Abdelouahid, Challal, Yacine·2020·Neurocomputing

DOI 10.1016/j.neucom.2019.11.041

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Authors: Boulemtafes, A., Derhab, A., & Challal, Y.
Affiliations: University of Béjaïa (DZ)
King Saud University (SA)
Centre de Recherche sur l'Information Scientifique et Technique (DZ)
Corresponding author: Boulemtafes, Amine
ORCID: Boulemtafes, Amine
Derhab, Abdelouahid
Challal, Yacine
Venue: Neurocomputing
Publisher: Elsevier BV
Published: 2020-04
Volume: 384
Pages: 21-45
ISSN: 0925-2312 (ISSN-L)
DOI: 10.1016/j.neucom.2019.11.041
OpenAlex: W2993032149
Cited by: 211
References: 80
Citations by year: 2026: 13 · 2025: 37 · 2024: 57 · 2023: 41 · 2022: 26 · 2021: 27 · 2020: 10
FWCI: 7.27
Type: journal article
Language: en
Open access: green
hal.science
Licence: other-oa
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: Neurocomputing (publishedVersion)
HAL (submittedVersion, other-oa)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
Physical Sciences › Computer Science › Computer Networks and Communications › Cryptography and Data Security
Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
Keywords: privacy-preserving deep learning, differential privacy, federated learning, secure multi-party computation, privacy-preserving machine learning, sensitive data protection

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