Publication Type

Journal Article

Version

acceptedVersion

Publication Date

11-2023

Abstract

The mobile Internet of Vehicles (IoVs) has great potential for intelligent transportation, and creates spatial data query demands to realize the value of data. Outsourcing spatial data to a cloud server eliminates the need for local computation and storage, but it leads to data security and privacy threats caused by untrusted third-parties. Existing privacy-preserving spatial range query solutions based on Homomorphic Encryption (HE) have been developed to increase security. However, in the single server model, the private key is held by the query user, which incurs high computation and communication burdens on query users due to multiple rounds of interactions. Moreover, exposing data access patterns to semi-honest servers is highly vulnerable to frequency and statistical attacks. To solve these issues, in this paper we propose a secure spatial location query within arbitrary geometric range while protecting access pattern. Specifically, we apply Paillier algorithm and polynomial fitting technique to achieve secure arbitrary geometric range query, design secure and efficient search protocol to hide data access patterns and alleviate query users from high computation and communication burdens under dual-server model. Formal security analysis shows that our scheme is secure under semi-honest model, and extensive experiments demonstrate that our work can reduce users' communication costs by more than 90% compared to previous schemes under single server model, which is practice in real-world scenarios.

Keywords

Access pattern, Cloud computing, dual-server model, Encryption, geometric range query, Mobile computing, spatial data query

Discipline

Information Security | Transportation

Research Areas

Cybersecurity

Publication

IEEE Transactions on Mobile Computing

First Page

1

Last Page

15

ISSN

1536-1233

Identifier

10.1109/TMC.2023.3336621

Publisher

Institute of Electrical and Electronics Engineers

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TMC.2023.3336621

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