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

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 article 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 sublinear 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.

Automated summary

With the maturity of Internet-of-Things technology, location-based service (LBS) is rapidly developing in intelligent terminal devices, but the large amount of spatial data generated poses a burden on providers; outsourcing spatial data to cloud server has become a new trend, but faces issues of data disclosure and query disclosure; the proposed ARQ scheme allows efficient range query while protecting data and user privacy, applicable to various forms of spatial data, and has practical significance.