Paper Contents
Abstract
Cloud computing encourages data owners to shift their sophisticated data management systems to commercial public clouds for greater flexibility and cost savings. For privacy, sensitive data must be encrypted before outsourcing, making plaintext keyword search obsolete. Hence, encrypted cloud data search is needed. Due to the enormous quantity of cloud data users and documents, the search service must enable multi-keyword queries and result similarity ranking. Searchable encryption studies usually focus on Boolean keyword searches or single-term searches without distinguishing search results. This research defines and solves the tough challenge of privacy-preserving multi-keyword ranking ontology keyword mapping and search over encrypted cloud data for the first time (BKCM). For a secure cloud data consumption system, we set strict privacy standards. "Boolean keyword coordinate matching," or as many matches as feasible, captures the similarity between the search query and data documents. "Inner product similarity" quantifies this similarity principle. We start with a safe inner product computing-based BKCM system. This technology is greatly expanded to suit privacy needs in two threat situations. Tests on real-world datasets reveal that the recommended solutions do not considerably raise computation or transmission costs.
Copyright
Copyright © 2023 B. Arunthamizharasi. This is an open access article distributed under the Creative Commons Attribution License.
