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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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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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