Privacy-Preserving Spatial Keyword Search With Lightweight Access Control in Cloud Environments

As cloud computing continues to gain popularity, various applications have been deployed under Industrial Internet of Things (IIoT) scenarios. In order to alleviate the heavy burden of local storage and processing, a substantial amount of data is entrusted to the cloud server (CS), but attendant security risks like privacy leakages begin to appear. In addition, another vital security issue, access control, has come to attention. Many existing spatial keyword similarity search schemes are unable to implement access control. To solve these issues, we propose a novel scheme privacy-preserving spatial keyword similarity search with lightweight access control (PSKSSA) scheme. Specifically, we design an efficient access control IR-tree (ACIR-tree) that achieves sublinear query efficiency. Access control is implemented through role-based polynomial technology, which is integrated into the ACIR-tree and the query vector, so that spatial keywords and access control information are uniformly encoded into a vector. Meanwhile, privacy is protected by enhanced asymmetric scalar-product-preserving encryption (EASPE), which guarantees indistinguishability against the chosen-plaintext attack (IND-CPA) model. The most similar $k$ results are found by the CS while implementing access control for data users. Through formal analysis and extensive experiments, it has proved that the proposed scheme is safe and effective, with good scalability.