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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]
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“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]
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“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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“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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