Accurate Range Query with Privacy Preservation for Outsourced Location-Based Service in IoT

Zhaoman Liu, Lei Wu*, Weizhi Meng, Hao Wang, Wei Wang

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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With the maturity of Internet of Things technology, location-based service (LBS) is developing rapidly in intelligent terminal devices, and it brings new vitality to the fields of logistics, transportation, product traceability and so on. The popularity of LBS produces a lot of spatial data, which inevitably
brings burden to the storage and management of LBS provider (LBSP). With the help of cloud computing and cloud storage, outsourcing spatial data to cloud server has become a new trend. However, due to the cloud server is not trusted, data outsourcing will face the problems of data disclosure and query disclosure. Range query is a common query in LBS, considering the situation of data outsourcing, this paper proposes an accurate range query (ARQ) scheme, which can realize efficient range query while preserving LBSP’s data privacy and user’s query privacy from being disclosed to the cloud server. The ARQ scheme is suitable for spatial data in any form without being limited to the case that the data points are only integers, which has a
certain practical significance. In addition, by dividing the region into atomic regions, ARQ can realize sub-linear search time and ensure dynamic update of spatial data. We proved the security of the proposed scheme through security analysis, and demonstrated the effectiveness of the scheme through experiments.
Original languageEnglish
JournalIEEE Internet of Things Journal
Issue number18
Pages (from-to)14322 - 14337
Publication statusPublished - 2021


  • IoT
  • LBS
  • Range query
  • Privacy preservation
  • Data outsourcing
  • Hilbert curve
  • SSW


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