Improved Indexing and Advanced Relevance Ranking Score for Multi-Keyword Search over Encrypted Cloud Data

Cloud computing is used to outsource the large volume of and important o most sensitive data on the remote server that is cloud server. To provide the data confidentiality and privacy, the sensitive cloud data have to be outsourced in encrypted format on commercial public cloud or private cloud. Traditional encryption techniques is used to securely search over encrypted data through single keyword search with rank score of the files also it supports the multiple keyword search but the sum of relevance score of the files is preserved and will not meet the effective data utilization need and the requirement of the large number of users and the large database. In this paper we solve the problem of preserving sum of multiple keywords search files ranking score. The existing ranking algorithm doesnt use the OR of multi-keywords. Also we enhance the relevance ranking score of the files which enhances the system usability by giving relevance ranking instead of sending undifferentiated results. We explore the statistical measure approach i. e. improved relevance score, from the information retrieval to build the index of files and develop the advanced ranking function and the Advanced search to protect the sum of the sensitive score information by improving the index structure. The resulting design able to do the multiple keyword searches without losing the privacy of the multiple keywords and prevent the sum of the relevance score of multi-keyword from the server from leakage. Through analysis this solution gives the strong security guarantee for the compared to the previous searchable encryption scheme for multi-keyword search using OPM function. The experimental result demonstrates the efficiency of the proposed solution, with it reduces the number of distinct keywords and results in reduction of index size and improves the relevance ranking score function and gives the most relevant document to users.