Framework of data privacy preservation and location obfuscation in vehicular cloud networks

Hani Al-Balasmeh*, Maninder Singh, Raman Singh

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The leakage of sensitive data of users and global positioning system location trajectories from different cloud computation sources have increased in daily lives. This critical problem can influence users' lives because attackers try to misuse the leaked information. In this study, we addressed the critical problem pertaining to the privacy of personal identification information (PII) and location information over vehicular cloud networks (VCNs). The problem was addressed by proposing a data and location privacy (DLP) framework that preserves data privacy in terms of the anonymity of PII and provides location privacy for users' trajectories through the obfuscation technique. The proposed DLP framework contains five stages: registration with VCN server, processing of PII/data privacy block, segregation of obfuscation data, authentication, and provision of cryptographic security. The result analysis shows that the proposed mechanism is able to generate obfuscated trajectories and successfully recomputes the original trajectories at the server side.
Original languageEnglish
Article numbere6682
Number of pages21
JournalConcurrency and Computation: Practice and Experience
Volume34
Issue number5
Early online date29 Oct 2021
DOIs
Publication statusPublished - 27 Jan 2022
Externally publishedYes

Keywords

  • applied cryptography
  • authentication
  • data and location privacy
  • privacy preservation
  • vehicular cloud network

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