Multi-Keyword Ranked Search in Cloud Environment.

Nowadays, with the simplification of cloud storage complexity and expanding its capacity, the cloud has become more widely accessible. Data owners increasingly shift away from traditional systems to leverage cloud resources, enhancing capacity and geographic diversity. Despite these advancements, the need to safeguard confidential data prompts a transformation to encryption before outsourcing, establishing additional standards for data privacy. This shift renders conventional data utilization through standard keyword searching obsolete. Therefore, there is a crucial need for an encrypted cloud information search service. To address the challenge of privacy-preserving multi-keyword searches, this paper proposes an efficient solution that enhances the existing framework. The proposed system uses an advanced encryption standard (AES) to improve data security and management in a cloud environment. Each private server maintains a registered information file and deletes merged data from the free cloud after observation. Customers can manage order records in the cloud data warehouse by transparently logging observations. Access to server information requires client inspection requests and verification. Private servers validate capacity and issue tokens to the Key Distribution Center (KDC) for key distribution. Finally, the term frequency-inverse dense frequency (TF-IDF) equation is used for data positioning. This approach ensures secure, authenticated, and efficient data handling. This solution focuses on two key objectives: firstly, incorporating synonym-based search to facilitate multi-keyword synonym queries, and secondly, implementing a ranked search to yield more detailed search results. While existing search methods support only fuzzy keywords or precise queries over encrypted cloud data. The authors assess its performance by employing real-life datasets, and comprehensive experiments affirm the validity and practicality of the proposed scheme. [ABSTRACT FROM AUTHOR]