Multi-User Privacy-Preserving and Verifiable Spatial-Feature Data Query for IoT Clouds
Xinsheng Chen, Xiaochao Wei, Hao Wang, Lijuan Xu · IEEE Internet of Things Journal · 2025
The large volume of spatial feature data generated by Internet of Things (IoT) devices is increasingly utilized in business location planning (BLP) services. reverse nearest neighbor (RNN) query techniques assist BLP in achieving more efficient business decisions by identifying candidate locations that are most attractive to users. However, existing RNN query schemes face significant challenges. First, there are some issues with data security and result integrity, as cloud servers can be both untrustworthy and malicious. Second, traditional query schemes commonly assume that the data users (DUs) are fully trusted and hold the key provided by the data owner (DO, IoT device users). In practice, however, once a DU’s key is compromised, the dataset of the DO is at risk. Regarding the above issues, this article proposes a privacy-preserving spatial feature data query scheme that supports multiple users without requiring key sharing. The proposed scheme is demonstrated using RNN queries, which are highly applicable in BLP services. Specifically, we first design a Quad-Tree for indexing spatial feature data. Then, we embed replicated secret sharing (RSS) technique into distributed two trapdoors public-key cryptosystem (DT-PKC) for key sharing. And based on this, a set of secure protocols that satisfy the mutual independence of DUs are designed for computing spatial distance and feature similarity. Finally, rigorous theoretical proofs and extensive experimental evaluations ensure the security and effectiveness of the scheme.