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.
“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]
The indexed Abstract says that using clouds and collaborative model training can come at the expense of privacy when sensitive data are processed or models are shared. It does not address third-party breaches, the Internet of Vehicles (IoV), or threats to vehicle security, so those parts of the cited sentence are unsupported. (LLM, conf=0.84)
Experimental — accuracy not yet measuredAdvanced PPML techniques such as FL, HE, SMPC, and DP are being thoroughly studied to protect user data and model integrity in the ML pipeline.
“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]
The indexed Abstract (page not given) says the paper reviews privacy-preserving deep-learning techniques and identifies risks to sensitive data during training, prediction, and model sharing. The available passages do not name FL, HE, SMPC, or DP, or say these techniques protect model integrity. (LLM, conf=0.72)
Experimental — accuracy not yet measuredPrivacy risks can arise during the training and inference phases of the ML pipeline in an IoV application, compromising confidentiality and integrity.
“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]
The Abstract says that processing sensitive data during training and prediction can create privacy risks, supporting the claim about those two phases. It does not address IoV applications or say that these risks compromise system integrity. (LLM, conf=0.86)
Experimental — accuracy not yet measuredData poisoning attacks are a significant risk in IoV.
“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]
The available indexed passage describes privacy risks in deep-learning training but does not address data-poisoning attacks or IoV. Searches for training-data poisoning and for Internet of Vehicles safety returned only the abstract and repository metadata, with no passage supporting the attributed risk. (LLM, conf=0.72)
Experimental — accuracy not yet measured
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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
- 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 › Artificial Intelligence › 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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