Efficient Multi - Keyword Ranked Search over Encrypted Cloud Computing

Cloud computing allow customer to store their data on remote site so it reduce burden on local complex data storing. But before storing sensitive data it can encrypted and this can overcome plaintext keyword search.AS large number of user and data on cloud and for search on that data allow multi keyword search also provide result similarity ranking for effective retrieval of data. From number of multi-keyword semantics to identify similarity between search query and data highly efficient rule of coordinate matching, i.e., as many matches as possible, and then use inner data similarity for quantitatively similarity measure. In this system, we define and solve the challenging problem of privacy-preserving multi-keyword ranked search over encrypted cloud data (MRSE),and establish a set of strict privacy requirements for such a secure cloud data utilization system to be implemented in real. We first propose basic idea of different privacy preserving multi-keyword search technique along with search on data that store on cloud in encrypted form and maintaining the integrity of rank order in search result and the cloud server is untrusted. .By hiding the user’s identity, the confidentiality of user’s data is maintaine

Ranking based search in the encrypted cloud environment

Cloud computing is emerging as a promising technology for outsourcing of data and quality of data services. However, information which is sensitive when upload on cloud eventually cause privacy problems. Data encryption provides security of data to some level, but at the cost of compromised efficiency. This paper focus on addressing data privacy problems. For the first time, the privacy issue is formulated from the aspect of similarity relevance of data and scheme robustness. Privacy of data is not assured if Server-side ranking based on order-preserving encryption is maintained. For the assurance of data privacy, multi-keyword ranked search over encrypted data in cloud computing (MRSE) scheme is proposed which supports top-k multi keyword retrieval. In MRSE, vector space model and Homomorphic encryption were employed. The vector space model helps to provide accuracy sufficient search of data and the Homomorphic encryption enables users to involve in the encryption of data. The majority of computing work is done on the server side. As a result, leakage of information can be eliminated and data security is ensured. DOI: 10.17762/ijritcc2321-8169.15028

Secure and Reliable Data Outsourcing in Cloud Computing

The many advantages of cloud computing are increasingly attracting individuals and organizations to outsource their data from local to remote cloud servers. In addition to cloud infrastructure and platform providers, such as Amazon, Google, and Microsoft, more and more cloud application providers are emerging which are dedicated to offering more accessible and user friendly data storage services to cloud customers. It is a clear trend that cloud data outsourcing is becoming a pervasive service. Along with the widespread enthusiasm on cloud computing, however, concerns on data security with cloud data storage are arising in terms of reliability and privacy which raise as the primary obstacles to the adoption of the cloud. To address these challenging issues, this dissertation explores the problem of secure and reliable data outsourcing in cloud computing. We focus on deploying the most fundamental data services, e.g., data management and data utilization, while considering reliability and privacy assurance. The first part of this dissertation discusses secure and reliable cloud data management to guarantee the data correctness and availability, given the difficulty that data are no longer locally possessed by data owners. We design a secure cloud storage service which addresses the reliability issue with near-optimal overall performance. By allowing a third party to perform the public integrity verification, data owners are significantly released from the onerous work of periodically checking data integrity. To completely free the data owner from the burden of being online after data outsourcing, we propose an exact repair solution so that no metadata needs to be generated on the fly for the repaired data. The second part presents our privacy-preserving data utilization solutions supporting two categories of semantics - keyword search and graph query. For protecting data privacy, sensitive data has to be encrypted before outsourcing, which obsoletes traditional data utilization based on plaintext keyword search. We define and solve the challenging problem of privacy-preserving multi- keyword ranked search over encrypted data in cloud computing. We establish a set of strict privacy requirements for such a secure cloud data utilization system to become a reality. We first propose a basic idea for keyword search based on secure inner product computation, and then give two improved schemes to achieve various stringent privacy requirements in two different threat models. We also investigate some further enhancements of our ranked search mechanism, including supporting more search semantics, i.e., TF × IDF, and dynamic data operations. As a general data structure to describe the relation between entities, the graph has been increasingly used to model complicated structures and schemaless data, such as the personal social network, the relational database, XML documents and chemical compounds. In the case that these data contains sensitive information and need to be encrypted before outsourcing to the cloud, it is a very challenging task to effectively utilize such graph-structured data after encryption. We define and solve the problem of privacy-preserving query over encrypted graph-structured data in cloud computing. By utilizing the principle of filtering-and-verification, we pre-build a feature-based index to provide feature-related information about each encrypted data graph, and then choose the efficient inner product as the pruning tool to carry out the filtering procedure

Multi-keyword Ranked Search over Encrypted Cloud Data Using RSA Algorithm

Ever since Cloud computing introduced, data owners are motivated to outsource their complex data management systems from local sites to the commercial public cloud for great flexibility and economic savings. But for protecting data privacy, sensitive data have to be encrypted before outsourcing, which obsoletes traditional data utilization based on plaintext keyword search. Thus, enabling an encrypted cloud data search service is of paramount importance. Considering the large number of data users and documents in the cloud, it is necessary to allow multiple keywords in the search request and return documents in the order of their relevance to these keywords. Related works on searchable encryption focus on single keyword search or Boolean keyword search, and rarely sort the search results. In this paper, for the first time, we define and solve the challenging problem of privacy-preserving multi-keyword ranked search over encrypted data in cloud computing (MRSE). We establish a set of strict privacy requirements for such a secure cloud data utilization system. Among various multi-keyword semantics, we choose the efficient similarity measure of “coordinate matching,” i.e., as many matches as possible, to capture the relevance of data documents to the search query. We further use “inner product similarity” to quantitatively evaluate such similarity measure. We first propose a basic idea for the MRSE based on secure inner product computation, and then give two significantly improved MRSE schemes to achieve various stringent privacy requirements in two different threat models. To improve search experience of the data search service, we further extend these two schemes to support more search semantics. Thorough analysis investigating privacy and efficiency guarantees of proposed schemes is given. Experiments on the real-world data set further show proposed schemes indeed introduce low overhead on computation and communication

A Note on One Privacy-Preserving Multi-Keyword Ranked Search Scheme over Encrypted Cloud Data

We show that the scheme [IEEE TPDS, 25(1), 2014, 222-233] fails, because the introduced similarity scores do not represent the true similarities between the indexing vectors and the querying vector. The returned documents by the cloud server are not indeed related to the queried keywords

A practical and secure multi-keyword search method over encrypted cloud data

Cloud computing technologies become more and more popular every year, as many organizations tend to outsource their data utilizing robust and fast services of clouds while lowering the cost of hardware ownership. Although its benefits are welcomed, privacy is still a remaining concern that needs to be addressed. We propose an efficient privacy-preserving search method over encrypted cloud data that utilizes minhash functions. Most of the work in literature can only support a single feature search in queries which reduces the effectiveness. One of the main advantages of our proposed method is the capability of multi-keyword search in a single query. The proposed method is proved to satisfy adaptive semantic security definition. We also combine an effective ranking capability that is based on term frequency-inverse document frequency (tf-idf) values of keyword document pairs. Our analysis demonstrates that the proposed scheme is proved to be privacy-preserving, efficient and effective