Privacy preservation in Distributed Deep Learning: A survey on Distributed Deep Learning, privacy preservation techniques used and interesting research directions
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Data security is another critical challenge, as the transmission and storage of sensitive data create vulnerabilities that may lead to breaches, exposing both individual and aggregate information.
“Data security is another critical challenge, as the transmission and storage of sensitive data create vulnerabilities that may lead to breaches, exposing both individual and aggregate information [7, 12, 22].”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [7]
Data breaches within third-party services could lead to the loss of proprietary vehicle models or private data, compromising user privacy and vehicle security on a large scale.
“Data breaches within third-party services could lead to the loss of proprietary vehicle models or private data, compromising user privacy and vehicle security on a large scale [7, 12, 22].”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [7]
[7] and [22] surveyed distributed DL alongside privacy-preservation techniques and their optimization.
“In the context of distributed systems, [7] and [22] surveyed distributed DL alongside privacy-preservation techniques and their optimization.”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [7]
Cites
Ehteshami Bejnordi, B., Veta, M., Johannes van Diest, P., van Ginneken, B., Karssemeijer, N., Litjens, G., van der Laak, J. A. W. M., and the CAMELYON16 Consortium, Hermsen, M., Manson, Q. F., et al. (2017) · JAMA · doi:10.1001/jama.2017.14585 · Cited by 3,293 · see citing works
Federated Learning With Differential Privacy: Algorithms and Performance Analysis
Wei, K., Li, J., Ding, M., Ma, C., Yang, H. H., Farokhi, F., Jin, S., Quek, T. Q. S., & Vincent Poor, H. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.2988575 · Cited by 2,400 · see citing works
Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning
Hitaj, B., Ateniese, G., & Perez-Cruz, F. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3134012 · Cited by 1,377 · see citing works
Tikkinen-Piri, C., Rohunen, A., & Markkula, J. (2018) · Computer Law & Security Review · doi:10.1016/j.clsr.2017.05.015 · Cited by 460 · see citing works
Cruz-Roa, A. A., Arevalo Ovalle, J. E., Madabhushi, A., & González Osorio, F. A. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40763-5_50 · Cited by 403 · see citing works
HIPAA regulations - a new era of medical-record privacy?
Annas, G. J. (2003) · New England Journal of Medicine · doi:10.1056/nejmlim035027 · Cited by 402 · see citing works
A review of privacy-preserving techniques for deep learning
Boulemtafes, A., Derhab, A., & Challal, Y. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.11.041 · Cited by 211 · see citing works
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Islam, N., & Zulkernine, M. (2025) · arXiv · doi:10.48550/arxiv.2503.01089 · Cited by 1 · see citing works
“Data security is another critical challenge, as the transmission and storage of sensitive data create vulnerabilities that may lead to breaches, exposing both individual and aggregate information [7, 12, 22].”
- Authors
- Antwi-Boasiako, E., Zhou, S., Liao, Y., Liu, Q., Wang, Y., & Owusu-Agyemang, K.
- Affiliations
- University of Electronic Science and Technology of China (CN)
- Corresponding author
- Liao, Yongjian
- Venue
- Journal of Information Security and Applications
- Publisher
- Elsevier BV
- Published
- 2021-09
- Volume
- 61
- Pages
- 102949
- ISSN
- 2214-2126 (ISSN-L)
- OpenAlex
- W3194293594
- Cited by
- 31
- References
- 7
- Citations by year
- 2025: 3 · 2024: 13 · 2023: 9 · 2022: 5 · 2021: 1
- FWCI
- 2.47
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Information Security and Applications (publishedVersion)
- Funders
- Sichuan Province Science and Technology Support Program (2019YFG0399)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Keywords
- distributed deep learning, collaborative deep learning, privacy preservation, homomorphic encryption, cryptographic techniques, peer learning, training data privacy, collusion resistance, communication and computation cost
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