AI-enabled privacy-preservation phrase with multi-keyword ranked searching for sustainable edge-cloud networks in the era of industrial IoT.

The integration of sensing technologies and cloud computing signifies the design perspectives of electronic healthcare systems. It has its own application domain to upload the clinical data of the patients and treatment procedures to the cloud server. Moreover, the data user may process the queries with suitable sensing parameters to obtain an appropriate medical record. As a result, the development of the Industrial Internet of Things (I-IoT) demands practical insights, trustworthiness, and reliability of intelligent automation to prevent the occurrence of potential risks in the process of production. In the past, multi-keyword searching (MKS) over encrypted cloud data has attracted researchers' attention. As cloud computing is highly practicing, data owners may easily outsource any kind of system data to commercial sites using the Industrial Internet of Things (I-IoT). However, data privacy and protection should be ensured using encryption techniques before any sensitive data is outsourced over insecure public networks. Providing cloud data encryption and secure keyword searching still exist as challenging issues. In I-IoT, cloud computing deals with a large amount of data users and documents, thus a technique like MKS is highly necessitated to process the search request and secure query processing. Thus, this paper presents a privacy-preservation phrase with multi-keyword ranked searching (PPP-MKRS) that introduces optimized filtering, binary tree index structure, and conjunctive keyword search to achieve secure searching efficiency. The experimental analysis shows that the proposed PPP-MKRS scheme consumes less computation, storage, and verification time in comparison with other searching encryption techniques. [ABSTRACT FROM AUTHOR]