Variable Techniques for Ciphertext Retrieval through Encrypted Data

Abstract

In recent years, Cloud computing provides strong grip and flexible access on outsource data, cloud storage, data privacy is major concern from to outsource their data, authenticated users are allowed to access this storage to prevent important and sensitive data. For data protection and utilization, we encrypt our sensitive data before outsourced our data because cannot trust storage server, are un-trusty but on other hand, data retrieval in encrypted format from cloud, is challenging task for data utilization, was encrypted from plaintext to ciphertext, when retrieves from cloud storage. However, searchable encryption schemes used Boolean search but they are unable to make data utilization for huge data and failed to handle multi-users access to retrieve ciphertext from cloud and user’s authentication. In this paper, we are using ranked keyword search over encrypted data by going k-documents at storage and using a Hierarchical Clustering Method is designed to guide more search semantics with an additional feature of making the system to cope the demand for fast ciphertext k-search in large scale environments explored the relevance score such as massive and big cloud data. This threshold splits the consequential clusters into sub-clusters until the necessity on the maximum size of cluster is reached. To make fetching search to be secure and privacy-preserving, it is built an index for searching on cloud data and retrieve the most relevant files from cloud. To defending privacy breaches from unauthorized users, users will go through authentication process and data retrieval time as well.