Euclidean Distance Based Similarity Measurement and Ensuing Ranking Scheme for Document Search from Outsourced Cloud Data

Abstract

In this paper, we propose the Euclidean Distance based Similarity Measurement and Ensuing Ranking (EDSMER) scheme to aid effective document search from outsourced cloud data. It is another attempt to find an alternative to binary based approaches. In this approach, the User or the Data owner needs to filter out the suitable keywords for the document and then the index is prepared. To provide security and privacy, both the data and the index are encrypted and moved to the cloud space. The application of Euclidean Distance based Similarity Measurement and Ensuing Ranking (EDSMER) scheme for document searching takes place after the authorized user requests for the documents through query terms. Initially the authorized user sends a query to Cloud Service Provider to retrieve all the documents which are mapped with the keywords provided by him. The proposed algorithm calculates the distance between the query terms and the index terms. The minimum the distance, the more it is closer towards each other and vice-versa. Our Euclidean Distance based Similarity Measurement and Ensuing Ranking (EDSMER) scheme greatly enhances the system functionality by sending the most relevant documents instead of transmitting all documents back. The experimental validations are performed on RFC and FIRE dataset. Through experimental analysis, we prove that our proposed approach is secure and efficient as well as exhibits better recall and precision rate in the IR system to deal with the document-retrieval process.