Privacy-Preserving Threshold Spatial Keyword Search in Cloud-Assisted IIoT
Yutao Yang, Yinbin Miao, Zuobin Ying, Jianting Ning, Xiangdong Meng, Kim‐Kwang Raymond Choo
Abstract
Cloud-assisted Industrial Internet of Things (IIoT) systems are increasingly deployed in various applications such as location-based services. Outsourcing data to cloud servers can help minimize local data storage and computation overheads, but it may introduce security and privacy concerns. Therefore, privacy-preserving spatial keyword search has been extensively explored in the literature. However, existing solutions still reveal the order of the spatio-textual similarity values between the query point and all data objects, and do not support searching for arbitrary geometric regions. To solve these issues, in this article we propose a privacy-preserving threshold spatial keyword search (TSKS) scheme. Specifically, we use the polynomial fitting technology, vector space model, and randomizable matrix multiplication technology to allow the cloud server to find relevant objects that are within some arbitrary geometric range and contain all query keywords. Finally, formal security analysis proves that our scheme can protect the privacy of data sets and queries, and extensive experiments demonstrate that our scheme is efficient and practical.