期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2015
卷号：3
期号：11
DOI：10.15680/IJIRCCE.2015.0311111
出版社：S&S Publications
摘要：The advent of distributed systems, data owners are motivated to outsource their complex datamanagement systems from local sites to commercial public cloud for great flexibility and economic savings. But forprotecting data privacy, sensitive data has to be encrypted before outsourcing which obsoletes traditional datautilization based on plaintext keyword search. Thus, enabling an encrypted cloud data search service is of paramountimportance. Considering the large number of data users and documents in cloud, it is crucial for the search service toallow multi-keyword query and provide result similarity ranking to meet the effective data retrieval need. Relatedworks on searchable encryption focus on single keyword search or Boolean keyword search and rarely differentiate thesearch results. In this paper, for the first time to define and solve the challenging problem of privacy-preserving multikeywordranked search over encrypted cloud data (MRSE) and establish a set of strict privacy requirements for such asecure cloud data utilization system to become a reality. Among various multi-keyword semantics, choose the efficientprinciple of “coordinate matching” i.e., as many matches as possible to capture the similarity between search query anddata documents and further use “inner product similarity” to quantitatively formalize such principle for similaritymeasurement. This paper propose a basic MRSE scheme using secure inner product computation and then significantlyimprove it to meet different privacy requirements in two levels of threat models. Thorough analysis investigatingprivacy and efficiency guarantees of proposed schemes is given and experiments on the real-world dataset further showproposed schemes indeed introduce low overhead on computation and communication.Proposed a multi-keywordsearch based on ranking using selection algorithm in clustering method. Double Encryption is used to secure data. AES& DES algorithm is used for encryption.
