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A Flexible and Privacy-Preserving Collaborative Filtering Scheme in Cloud Computing for VANETs

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Published:22 October 2021Publication History
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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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  1. A Flexible and Privacy-Preserving Collaborative Filtering Scheme in Cloud Computing for VANETs

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    • Published in

      cover image ACM Transactions on Internet Technology
      ACM Transactions on Internet Technology  Volume 22, Issue 2
      May 2022
      582 pages
      ISSN:1533-5399
      EISSN:1557-6051
      DOI:10.1145/3490674
      • Editor:
      • Ling Liu
      Issue’s Table of Contents

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 22 October 2021
      • Accepted: 1 September 2020
      • Revised: 1 August 2020
      • Received: 1 June 2020
      Published in toit Volume 22, Issue 2

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