The DOI matches its Crossref record; 75 of the 80 references it cites were found in a registry or an index.
A review of privacy-preserving techniques for deep learning
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“The involvement of third-party services in data collection, model training, or deployment introduces additional vulnerabilities, as mishandling of data or breaches at third-party entities can threaten the entire IoV ecosystem and compromise vehicle security and user privacy on a broader scale [16].”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [16]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“To circumvent these privacy risks, advanced privacy-preserving ML (PPML) techniques such as federated learning (FL), homomorphic encryption (HE), secure multi-party computations (SMPC), and differential privacy (DP) are being thoroughly studied to protect user data and model integrity in the ML pipeline [16, 97, 161].”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [16]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In the ML pipeline of an IoV application, privacy risks can arise during the training and inference phases to compromise the confidentiality and integrity of the system [16, 97, 161].”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [16]
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In the citing paper
“data poisoning attacks involve adversaries manipulating training data to compromise model integrity or insert backdoors, a significant risk in IoV where data quality is crucial for safety”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [16]
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Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Learning Deep Architectures for AI
Bengio, Y. (2009) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000006 · Cited by 7,330
Deep Learning with Differential Privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016) · Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2976749.2978318 · Cited by 6,578
The Algorithmic Foundations of Differential Privacy
Dwork, C., & Roth, A. (2014) · Foundations and Trends® in Theoretical Computer Science · doi:10.1561/0400000042 · Cited by 5,557
Learning Deep Architectures for AI
Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 5,050
Practical Secure Aggregation for Privacy-Preserving Machine Learning
Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., & Seth, K. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3133982 · Cited by 3,956
The Algorithmic Foundations of Differential Privacy
Dwork, C., & Roth, A. (2013) · doi:10.1561/9781601988195 · Cited by 3,856
A survey of deep neural network architectures and their applications
Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2017) · Neurocomputing · doi:10.1016/j.neucom.2016.12.038 · Cited by 3,239
Decentralizing Privacy: Using Blockchain to Protect Personal Data
Zyskind, G., Nathan, O., & Pentland, A. '. (2015) · 2015 IEEE Security and Privacy Workshops · doi:10.1109/spw.2015.27 · Cited by 2,617
Privacy-preserving deep learning
Shokri, R., & Shmatikov, V. (2015) · Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2810103.2813687 · Cited by 2,356
Research works printed but not linked: 5
- N. Papernot, M. Abadi, U. Erlingsson, I. Goodfellow, and K. Talwar, Semi-supervised knowledge transfer for deep learning from private training data, arXiv:1610.05755v4, 2016. · arXiv
- Le Trieu Phong, 2017, Privacy-Preserving deep learning via additively homomorphic encryption, IACR Cryptol. ePrint Arch.
- S.A. Osia, A.S. Shamsabadi, A. Taheri, K. Katevas, H.R. Rabiee, N.D. Lane, and H. Haddadi, Privacy-preserving deep inference for rich user data on the cloud, arXiv:1710.01727v3, 2017. · arXiv
- S.A. Osia, A.S. Shamsabadi, A. Taheri, K. Katevas, S. Sajadmanesh, H.R. Rabiee, … & H. Haddadi, A hybrid deep learning architecture for privacy-preserving mobile analytics, arXiv:1703.02952, 2017. · arXiv
- J. Chi, E. Owusu, X. Yin, T. Yu, W. Chan, P. Tague, & Y. Tian, Privacy partitioning: protecting user data during the deep learning inference phase, arXiv:1812.02863, 2018. · arXiv
Cited by
Sorted by citations
Federated Learning for intrusion detection system: Concepts, challenges and future directions
Agrawal, S., Sarkar, S., Aouedi, O., Yenduri, G., Piamrat, K., Alazab, M., Bhattacharya, S., Maddikunta, P. K. R., & Gadekallu, T. R. (2022) · Computer Communications · doi:10.1016/j.comcom.2022.09.012 · Cited by 457
Differential Privacy for Deep and Federated Learning: A Survey
Ouadrhiri, A. E., & Abdelhadi, A. (2022) · IEEE Access · doi:10.1109/access.2022.3151670 · Cited by 445
Machine Learning for Healthcare Wearable Devices: The Big Picture
Sabry, F., Eltaras, T., Labda, W., Alzoubi, K., & Malluhi, Q. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/4653923 · Cited by 321
Privacy and Security Issues in Deep Learning: A Survey
Liu, X., Xie, L., Wang, Y., Zou, J., Xiong, J., Ying, Z., & Vasilakos, A. V. (2020) · IEEE Access · doi:10.1109/access.2020.3045078 · Cited by 311
Socially Responsible AI Algorithms: Issues, Purposes, and Challenges
Cheng, L., Varshney, K. R., & Liu, H. (2021) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12814 · Cited by 261
Metadata
- 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
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2020-04
- Volume
- 384
- Pages
- 21-45
- ISSN
- 0925-2312 (ISSN-L)
- 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
- 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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