Optimizing Encrypted Cloud Data Security and Searchability through Multi-Keyword Ranking Search Methods

Abstract

Encryption is becoming more and more crucial for protecting user privacy as cloud services gain popularity. It is essential to provide dependable methods for quick and safe data recovery. This study suggests a brand-new method for searching encrypted cloud data. The proposed technique uses a greedy depth-first search (DFS) algorithm combined with an advanced grading system to optimise queries including multiple words and synonyms. Users are assumed to search using a large number of keywords, some of which may be synonyms for article terms, according to the recommended architecture. To address this issue, a search algorithm that makes use of synonyms from user queries was developed. Greedy search techniques assist us in locating the most relevant data even if the search universe is constantly expanding. Our depth-first search approach increases the probability of discovering relevant data. Additionally, our research employs a novel ranking algorithm that evaluates a text’s relevance to a search query based on keyword proximity, synonym accuracy, and frequency. In simulated cloud architecture tests employing industry-standard protocols and encrypted datasets, our proposed technique performs better than the state-of-the-art approaches. Runtime, recall, and accuracy all demonstrate this advantage. The greedy Depth-First Search (DFS) method increases efficiency by optimising resources. By automatically sorting the results, a grading technique assists users in finding the most relevant articles fast. In protected cloud storage systems, this synonym-enhanced search method may boost privacy and usability now.