The DOI matches its Crossref record.
A Flexible and Privacy-Preserving Collaborative Filtering Scheme in Cloud Computing for VANETs
Abstract
The vehicular ad hoc network (VANET) has become a hot topic in recent years. With the development of VANETs, how to achieve secure and efficient machine learning in VANETs is an urgent problem to be solved. Besides, how to ensure that users obtain the accurate results of machine learning is also a challenge. Based on the homomorphic encryption and secure multiparty computing technology, a flexible and privacy-preserving collaborative filtering scheme is proposed to accomplish the personalized recommendation for users, which is based on users’ interests and locations. On the one hand, the data can be updated by users flexibly to ensure the freshness and accuracy of the dataset of interest. On the other hand, the weighted values of user interest can be safely sorted to improve the accuracy of collaborative filtering effectively. Moreover, a novel collaborative filtering algorithm based on the homomorphic encryption technology is designed, which can guarantee that the calculated decryption result by machine learning is the same as the plaintext. Note that the privacy of user data can be preserved during machine learning in this algorithm. Both theoretical and experimental analyses demonstrate that the proposed scheme is secure and efficient for collaborative filtering in cloud computing in VANETs.
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It employed a symmetric balanced incomplete block design (SBIBD) for efficient aggregation which ensured sensitive data remains encrypted.
“It employed a symmetric balanced incomplete block design (SBIBD) for efficient aggregation which ensured sensitive data remains encrypted.”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [162]
Study [162] proposed a privacy-preserving multi-party collaborative filtering system for IVI recommendations, using SMPC and the Paillier cryptosystem.
“The study [162] proposed a privacy-preserving multi-party collaborative filtering system for IVI recommendations, using SMPC and the Paillier cryptosystem.”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [162]
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Islam, N., & Zulkernine, M. (2025) · arXiv · doi:10.48550/arxiv.2503.01089 · Cited by 9
“Additionally, managing cumulative privacy loss over multiple queries or iterations (the privacy budget) is crucial to maintaining robust privacy guarantees over time.”
Metadata
- Authors
- Yang, H., Shen, J., Zhou, T., Ji, S., & Vijayakumar, P.
- Affiliations
- School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing, China
- Nanjing University of Information Science and Technology (CN)
- The Network Information Center, Nanjing University of Information Science & Technology, Nanjing, China
- Department of Computer Science & Engineering, University College of Engineering Tindivanam, India
- Venue
- ACM Transactions on Internet Technology
- Published
- 2021-10-22
- Volume
- 22
- Issue
- 2
- Pages
- 1-19
- ISSN
- 1533-5399 (ISSN-L)
- 1557-6051
- OpenAlex
- W3208037160
- Cited by
- 17
- References
- 0
- Citations by year
- 2025: 2 · 2024: 5 · 2023: 1 · 2022: 9
- FWCI
- 1.33
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Transactions on Internet Technology (publishedVersion)
- Funders
- National Natural Science Foundation of China (U1836115, 61672295, 61922045, and 61672290)
- Natural Science Foundation of Jiangsu Province (BK20181408)
- Foundation of State Key Laboratory of Cryptology (MMKFKT201830)
- Peng Cheng Laboratory Project of Guangdong Province (PCL2018KP004)
- CICAEET fund
- PAPD fund
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Cryptography and Data Security
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Vehicular Ad Hoc Networks (VANETs)
- Keywords
- vehicular ad hoc networks, collaborative filtering, privacy-preserving machine learning, homomorphic encryption, secure multi-party computation, personalized recommendation, location-based recommendation, cloud computing
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