This paper is published in Volume-3, Issue-1, 2016
Area
Parallel and Distributed Systems
Author
Arati Deshmukh, Dr. S. T. Singh, Prof. P. B. Sahane
Org/Univ
PK Technical Campus, Pune, India
Pub. Date
03 January, 2017
Paper ID
V3I1-1141
Publisher
Keywords
Searchable Encryption, Multi-Keyword Ranked Search, Dynamic Update, Cloud Computing.

Citationsacebook

IEEE
Arati Deshmukh, Dr. S. T. Singh, Prof. P. B. Sahane. A Secure and Dynamic Multi-keyword Ranked Search Scheme over Encrypted Cloud Data, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Arati Deshmukh, Dr. S. T. Singh, Prof. P. B. Sahane (2016). A Secure and Dynamic Multi-keyword Ranked Search Scheme over Encrypted Cloud Data. International Journal of Advance Research, Ideas and Innovations in Technology, 3(1) www.IJARIIT.com.

MLA
Arati Deshmukh, Dr. S. T. Singh, Prof. P. B. Sahane. "A Secure and Dynamic Multi-keyword Ranked Search Scheme over Encrypted Cloud Data." International Journal of Advance Research, Ideas and Innovations in Technology 3.1 (2016). www.IJARIIT.com.

Abstract

The major aim of this paper is to solve the problem of multi-keyword ranked search over encrypted cloud data (MRSE) at the time of protecting exact method wise privacy in the cloud computing concept. Data holders are encouraged to outsource their difficult data management systems from local sites to the business public cloud for large flexibility and financial savings. However, for protecting data privacy, sensitive data have to be encrypted before outsourcing, which performs traditional data utilization based on plain text keyword search. As a result, allowing an encrypted cloud data search service is of supreme significance. In view of a large number of data users and documents in the cloud, it is essential to permit several keywords in the search demand and return documents in the order of their appropriate to these keywords. The similar mechanism on searchable encryption makes center on single keyword search or Boolean keyword search and rarely sort the search results. In the middle of various multi-keyword semantics, deciding the well-organized similarity measure of “coordinate matching,” it means that as many matches as possible, to capture the appropriate data documents to the search query. Particularly, we consider “inner product similarity” i.e., a number of query keywords show in a document, to quantitatively estimate such match measure that document to the search query. Through the index construction, every document is connected with a binary vector as a sub-index where each bit characterize whether matching keyword is contained in the document. The search query is also illustrated as a binary vector where each bit means whether the corresponding keyword appears in this search request, so the matched one could be exactly measured by the inner product of the query vector with the data vector. On the other hand, directly outsourcing the data vector or the query vector will break the index privacy or the search privacy. The vector space model facilitates to offer enough search accuracy, and the DES encryption allows users to occupy in the ranking while the popularity of computing work is done on the server side by the process only on cipher text. As a consequence, data leakage can be eradicated and data security is guaranteed.