SEARCHABLE SYMMETRIC ENCRYPTION METHOD FOR ENCRYPTED DATA IN CLOUD

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1 JJT SEARCHABLE SYMMETRIC ENCRYPTION METHOD FOR ENCRYPTED DATA IN CLOUD P.Vidyasagar, R.Karthikeyan, Dr.C.Nalini M.Tech Student, Dept of CSE,Bharath University, .Id: Assistant Professor, Dept of CSE,Bharath University, Professor, Dept of CSE,Bharath University, ABSTRACT As Cloud Computing becomes prevalent, more and more sensitive information are being centralized into the cloud.for the protection of data privacy, sensitive data usually have to be encrypted before outsourcing, which makes effective data utilization a very challenging task. Thus, enabling an encrypted cloud data search service is of paramount importance. Considering the large number of data users and documents in cloud, it is crucial for the search service to allow multi-keyword query and provide result similarity ranking to meet the effective data retrieval need. Keywords -- multi-keyword ranked search(mrse), k-nearest neighbor (knn), order-preserving encryption (OPE), Two round search over encrypted (TRSE), Fully Homomorphic Encryption over the integers (FHEI) Introduction In this paper, we focus on multikeyword ranked search over encrypted cloud data (MRSE), and establish a set of strict privacy requirements for such a secure cloud data utilization system to become a reality. Among various multikeyword 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. Specifically, we use inner product similarity i.e., the number of query keywords appearing in a document, to quantitatively evaluate such similarity measure of that document to the search query. During the index construction, each document is associated with a binary vector as a sub-index where each bit represents whether corresponding keyword is contained in the document. The search query is also described as a binary vector 321

2 where each bit means whether corresponding keyword appears in this search request, so the similarity could be exactly measured by the inner product of the query vector with the data vector. However, directly outsourcing the data vector or the query vector will violate the index privacy or the search privacy.to meet the challenge of supporting such multikeyword semantic without privacy breaches, we propose a basic idea for the MRSE using secure inner product computation, which is adapted from a secure k-nearest neighbor (knn) technique and then give two significantly improved schemes in a step-by-step manner to achieve various stringent privacy requirements. Compared with the preliminary version of this paper, this journal version proposes two new mechanisms to support more search semantics. This version also studies the support of data/index dynamics in the mechanism design. Moreover, we improve the experimental works by adding the analysis and evaluation of two new schemes. In addition to these improvements, we add more analysis on secure inner product and the privacy part. Related works: Searchable encryption focus on single keyword search or Boolean keyword search, and rarely differentiate 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 cloud data (MRSE), and establish a set of strict privacy requirements for such a secure cloud data utilization system to become a reality. We first propose a basic MRSE scheme using secure inner product computation, and then significantly improve it to meet different privacy requirements in two levels of threat models. Thorough analysis investigating privacy and efficiency guarantees of proposed schemes is given, and experiments on the real-world dataset further show proposed schemes indeed introduce low overhead on computation and communication Objective Main objective of this project introduce new scheme employing the fully homomorphic encryption, which fulfills the security requirements of multi keyword top-k retrieval over the encrypted cloud data. Figure-1 Encryption and Dycryption process Existing System: The large number of data users and documents in cloud, it is crucial for the search service to allow multi-keyword query and provide result similarity ranking to meet the effective data retrieval need. The searchable encryption focuses on single keyword search or Boolean keyword search, and rarely differentiates the search results.the trivial solution of downloading all the data and decrypting 322

3 locally is clearly impractical, due to the huge amount of bandwidth cost in cloud scale systems.considering the potentially large number of on-demand data users and huge amount of outsourced data documents in the cloud, this problem is particularly challenging as it is extremely difficult to meet also the requirements of performance, system usability and scalability to meet the effective data retrieval need, the large amount of documents demand the cloud server to perform result relevance ranking, instead of returning undifferentiated results. Disadvantages: Undesirable security and privacy risks Single-Keyword search without ranking Boolean -Keyword search without ranking Only allow for Single keyword Search Top-k multi-keyword has been used but only allow for Boolean search. Proposed System: We define and solve the challenging problem of privacypreserving 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 become a reality. Among various multi-keyword semantics, we choose the efficient principle of coordinate matching.the majority of computing work is done on the cloud while the user takes part in ranking, which guarantees top-k multi keyword retrieval over encrypted cloud data with high security and practical efficiency. First attempt to formulate the privacy issue in searchable encryption, and show server-side ranking based on order-preserving encryption (OPE) inevitably violates data privacy.propose a Multi-keyword ranked search over encrypted cloud data (MRSE) scheme, which fulfills the secure multi keyword top-k retrieval over encrypted cloud data. Advantages: The new scheme guarantees high data privacy. Provide heavy security for storage Multi-keyword ranked search over encrypted cloud data(mrse) Lightweight communication and computation cost Figure-2. Architectural Design 323

4 Module Description Modules: Index Creation Module The data owner has a collection of n files to outsource onto the cloud server in encrypted form and expects the cloud server to provide keyword retrieval service to data owner himself or other authorized users. Data Encryption Module The encryption module guarantee the operability and security at the same time on server side. the original fully Homomorphic encryption scheme, which employs ideal lattices over a polynomial ring, is too complicated and inefficient for practical utilization. Fortunately, as a result of employing the vector space model to top-k retrieval, only addition and multiplication operations over integers are needed to compute the relevance scores from the encrypted searchable index. Therefore, can reduce the original homomorphism in a full form to a simplified form that only supports integer operations, which allows more efficiency. Vector Space Module The vector space model to identify the score on multi keyword search against cloud. The vector space model is an algebraic model for representing a file as a vector.moreover, it allows computing a continuous degree of similarity between queries and files, and then ranking files according to their relevance. It meets our needs of top-k retrieval.files can be ranked in order and, therefore, the most relevant files can be found Top- k Rank Provide Module Server-side ranking based on OPE violates the privacy of sensitive information, which is considered uncompromisable in the securityoriented third party cloud computing scenario, i.e., security cannot be tradeoff for efficiency. To achieve data privacy, ranking has to be left to the user side. due to the interaction between the server and the user including searchable index return and ranking score calculation. Thus, the user-side ranking schemes are challenged by practical use. A more server-siding scheme might be a better solution to privacy issues. TRSE-Query Process Module The cloud server receives a query consisting of multi keywords, it computes the scores from the encrypted index stored on cloud and then returns the encrypted scores of files to the data user. Next, the data user decrypts the scores and picks out the top-k highest scoring files identifiers to request to the cloud server. The retrieval takes a two-round communication between the cloud server and the data user. The TRSE scheme, in which ranking is done at the user side while scoring calculation is done at the server side. 324

5 Scope of Study Considering the large number of data users and documents in cloud, it is crucial for the search service to allow multi-keyword query and provide result similarity ranking to meet the effective data retrieval need. Figure-3. Data flow Diagram Methodology Two round search over encrypted (TRSE) A TRSE scheme, which fulfills the secure multi keyword top-k retrieval over encrypted cloud data. Specifically, for the first time, we employ relevance score to support multi keyword top-k retrieval.the framework of TRSE includes four algorithms: Setup, Index Build, TrapdoorGen, Score Calculate, and Rank. Order-preserving encryption (OPE) Server-side ranking based on order-preserving encryption (OPE) inevitably violates data privacy. Fully Homomorphic Encryption over the integers (FHEI) Fully Homomorphic Encryption over the integers can reduce the original homomorphism in a full form to a simplified form that only supports integer operations, which allows more efficiency than the full form does Conclusion In this paper, we define and solve the challenging problem of privacypreserving multi-keyword ranked search over encrypted cloud data by introducing the TRSE Algorithm which fulfills the secure multi keyword top-k retrieval over encrypted cloud data and establish a set of strict privacy requirements for such a secure cloud data utilization system to become a reality. References [1] I.H. Witten, A. Moffat, and T.C. Bell, Managing Gigabytes: Compressing and Indexing Documents and Images. Morgan Kaufmann Publishing, May [2] D. Song, D. Wagner, and A. Perrig, Practical Techniques for Searches on Encrypted Data, Proc. IEEE Symp. Security and Privacy, [3] A. Singhal, Modern Information Retrieval: A Brief Overview, IEEE Data Eng. Bull., vol. 24, no. 4, pp , Mar [4] E.-J. Goh, Secure Indexes, Cryptology eprint Archive, eprint.iacr.org/2003/216/2003. [5] D. Boneh, G.D. Crescenzo, R. Ostrovsky, and G. Persiano, Public Key Encryption with Keyword Search, Proc. Int l Conf. Theory and Applications of Cryptographic Techniques (EUROCRYPT),

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