Efficient LBS queries with mutual privacy preservation in IoV

Shushu Liu, An Liu*, Zheng Yan, Wei Feng

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

7 Citations (Scopus)


Public awareness on privacy stimulates many researches about privacy-preserving location based services (LBS) in terms of providing mutual privacy to both LBS and its users. However, the high latency of privacy preservation in LBS becomes a main obstacle for applying LBS to Internet of Vehicles (IoV). To solve this problem, we propose two privacy-preserving LBS query schemes (kNN and T-kNN) by taking the advance of fog computing and by applying oblivious transfer (OT) and ciphertext-policy attribute based encryption (CP-ABE). Given a query from a vehicle, both schemes return k nearest POIs as response, with the difference that T-kNN supports fine-grained type based POI queries. Based on our proposed oblivious key transfer and privacy-preserving secret key generation, both schemes preserve mutual privacy of both LBS provider and vehicles. Complexity analysis and empirical study show that our approach outperforms the other two state-of-the-art works.

Original languageEnglish
Pages (from-to)62-71
Number of pages10
JournalVehicular Communications
Publication statusPublished - 1 Apr 2019
MoE publication typeA1 Journal article-refereed


  • Fog computing
  • IoV
  • kNN query with type
  • LBS

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