Date of Publication :7th April 2015

Abstract: Information sharing is the key goal of Cloud Storage servers. It allows storage of sensitive and large volume of data with limited cost and high access benefits. Security must be given due importance for the cloud data with utmost care to the data and confidence to the data owner. But this limits the utilization of data through plain text search. Hence an excellent methodology is required to match the keywords with encrypted cloud data. The proposed approach similarity measure of “coordinate matching” combined with “inner product similarity” quantitatively evaluates and matches all relevant data with search keyword to arrive at best results .In this approach, each document is associated with a binary vector to represent a keyword contained in the document. The search keyword is also described as a binary vector, so the similarity could be exactly measured by the inner product of the query vector with the data vector. The inner product computation and the two multi-keyword ranked search over encrypted data (MRSE) schemes ensures data privacy and provides detailed information about the dynamic operation on the data set and index. To further refine these search results, “stop word” and “stemming” techniques have been used. Also, a checksum value for each page is stored using checksum validation algorithm and individual pages are updated in the cloud which reduces the usage cost and hence improves the experience of the user.

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