Top PDF A Review on Secure Multi Keyword Ranked Search for Multi Data Owners in Cloud Computing

A Review on Secure Multi Keyword Ranked Search for Multi Data Owners in Cloud Computing

A Review on Secure Multi Keyword Ranked Search for Multi Data Owners in Cloud Computing

The hunt facility and privacy protective over encoded cloud data are essential. If we study huge amount of data documents and data users in the cloud, it is hard for the necessities of performance, usability, plus scalability. Concerning to encounter the real data recovery, the huge amount of data documents in the cloud server achieve to outcome relevant rank instead of returning undistinguishable outcomes. Ranking scheme cares multiple keyword hunt to recover the hunt correctness. Today’s Google network hunt devices, data users offer set of keywords instead of unique keyword hunt importance to retrieve the maximum significant data. Coordinate matching is a synchronize pairing of query keywords which are relevance to that document to the query.
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Empirical Analysis of Secured Data Maintenance over Cloud Schema with Advanced Searching Environment

Empirical Analysis of Secured Data Maintenance over Cloud Schema with Advanced Searching Environment

In this system, a secure, efficient and dynamic search scheme is proposed, which supports not only the accurate multi-keyword ranked search but also the dynamic deletion and insertion of documents. We construct a special keyword balanced binary tree as the index, and propose a “Greedy Depth-first Search” algorithm to obtain better efficiency than linear search. In addition, the parallel search process can be carried out to further reduce the time cost. The security of the scheme is protected against two threat models by using the secure kNN algorithm. Experimental results demonstrate the efficiency of our proposed scheme. There are still many challenge problems in symmetric SE schemes. In the proposed scheme, the data owner is responsible for generating updating information and sending them to the cloud server. Thus, the data owner needs to store the unencrypted index tree and the information that are necessary to recalculate the IDF values. Such an active data owner may not be very suitable for the cloud computing model.
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Confidentiality Preserving Efficient and Dynamic Multi-Keyword Ranked Search over Encoded Multi Cloud Data

Confidentiality Preserving Efficient and Dynamic Multi-Keyword Ranked Search over Encoded Multi Cloud Data

ABSTRACT: In this system a special tree-based index structure is constructed and proposes a “Greedy Depth-first Search” algorithm to provide efficient multi-keyword ranked search. The proposed scheme can achieve sub-linear search time and deal with the deletion and insertion of documents flexibly. Extensive experiments are conducted to demonstrate the efficiency of the proposed scheme. Due to the increasing popularity of cloud computing, more and more data owners are motivated to outsource their data to cloud servers for great convenience and reduced cost in data management. However, sensitive data should be encrypted before outsourcing for privacy requirements, which obsoletes data utilization like keyword-based document retrieval. In this system, we present a secure multi-keyword ranked search scheme over encrypted cloud data, which simultaneously supports dynamic update operations like deletion and insertion of documents. Specifically, the vector space model and the widely-used TFIDF model are combined in the index construction and query generation. We construct a special tree-based index structure and propose a “Greedy Depth-first Search” algorithm to provide efficient multi-keyword ranked search. The secure kNN algorithm is utilized to encrypt the index and query vectors, and meanwhile ensure accurate relevance score calculation between encrypted index and query vectors. In order to resist statistical attacks, phantom terms are added to the index vector for blinding search results. Due to the use of our special tree-based index structure, the proposed scheme can achieve sub- linear search time and deal with the deletion and insertion of documents flexibly. Extensive experiments are conducted to demonstrate the efficiency of the proposed scheme.
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Secure and Efficient Multi-keyword Ranked Search Method on Cloud

Secure and Efficient Multi-keyword Ranked Search Method on Cloud

In this paper [5], the challenging issue of preserving the privacy of multi-keyword ranked search over encrypted information in cloud computing (MRSE) is proposed. A set of strict privacy requirements are designed for a secure cloud information usage framework. Coordinate matching is used to capture the relevance of data documents to the search query. Even the inner product similarity is used to quantitatively assess such similarity measure. A fundamental idea for the MRSE based on secure inner product computation is proposed 6. “Privacy-preserving multi-keyword text search in the cloud supporting similarity-based ranking
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Generating Secret Key for Multi Keyword Ranked Search Over Encrypted Cloud Data

Generating Secret Key for Multi Keyword Ranked Search Over Encrypted Cloud Data

security for the data owners and clients. As increasing popularity of cloud computing, more and more data owners are motivated to their data to cloud servers for great convenience and reduced cost in data management. So, sensitive data should be encrypted before outsourcing for privacy requirements, which obsoletes data utilization like keyword- based document retrieval. A secret key generation for multi-keyword ranked search over encrypted cloud data, which simultaneously dynamic update operations like modify and view of documents. Specifically, the vector space model and the widely-used TF IDF model are combined in the index construction and query generation. The secure kNN algorithm is utilized to encrypt the index and query vectors, and mean while ensure correct relevance score calculation between encrypted index and query vectors. In order to provide security, the secrete key is generated for each data owners individual electronic mail. That secrete key can be copied and pasted for further login. When the user wants to change their password the secret key also been updated. The user’s privacy more increased. Due to the use of secrete key security is highly increased, While outsourcing the data. User can search and find the document using search command that is displayed in Rank based.
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A Secure and Dynamic Multi-keyword Ranked Search Scheme over Encrypted Cloud Data

A Secure and Dynamic Multi-keyword Ranked Search Scheme over Encrypted Cloud Data

Due to the growing popularity of cloud computing, more and more data owners are motivated to outsource their data to cloud servers for great convenience and reduced cost in data management. However, sensitive data should be encrypted before outsourcing for privacy requirements, which obsoletes data utilization like keyword-based document retrieval. In this paper, we present a secure multi-keyword ranked search scheme over encrypted cloud data, which simultaneously supports dynamic update operations like deletion and insertion of documents. Specifically, the vector space model and the widely-used TF_IDF model are shared in the index construction and query generation. We construct a special tree-based index structure and propose a “Greedy Depth-first Search” algorithm to provide efficient multi-keyword ranked search. The secure kNN algorithm is utilized to encrypt the index and query vectors, and meanwhile ensure accurate relevance score calculation between encrypted index and query vectors. In order to defy statistical attacks, phantom terms are added to the index vector for blinding search results. Due to the use of our special tree-based index structure, the proposed scheme can attain sub-linear search time and deal with the deletion and insertion of documents flexibly. Extensive experiments are conducted to demonstrate the efficiency of the proposed scheme.
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Dynamic Secure Multi Keyword Ranked Search over Encrypted Cloud Data

Dynamic Secure Multi Keyword Ranked Search over Encrypted Cloud Data

2) Propose two plans taking after the standard of direction coordinating and internal item similitude. Also, we proposed the ready framework which will create cautions when the un-approved client tries to get the information from the cloud, the ready will produce as mail and message. furthermore, the DES encryption permits clients to possess in the ranking while the prominence of registering work is done on the server side by the procedure just on figure content which

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A Multi-keyword Ranked Search Scheme that is Dynamic and Secure over Cloud Data

A Multi-keyword Ranked Search Scheme that is Dynamic and Secure over Cloud Data

This paper proposes a safe tree-based search plan over the scrambled cloud information, which underpins multikeyword positioned inquiry and element operation on the archive gathering. In particular, the vector space model what's more, the broadly utilized "term frequency (TF) × inverse document frequency” model are consolidated in the list development and inquiry era to give multikeyword positioned seek. So as to acquire high inquiry productivity, we build a tree-based file structure and propose an "greedy depth first search" calculation based on this record tree. Because of the unique structure of our tree-based record, the proposed seek plan can adaptably accomplish sub-straight pursuit time and manage the cancellation and insertion of archives. Our commitments are condensed as takes after:
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Secure Fuzzy Multi-Keyword Ranked Search over Encrypted Cloud Data

Secure Fuzzy Multi-Keyword Ranked Search over Encrypted Cloud Data

ABSTRACT: As cloud computing has become prevalent data owners tend to outsource their sensitive information to the cloud. The data have to be encrypted before outsourcing to protect the data privacy. Related works on searchable encryption support either single keyword search or Boolean keyword search which focuses on exact keyword. It does not support minor typos or format inconsistencies. Sorting of results is also rare. In this paper we propose secure fuzzy multi keyword ranked search over encrypted cloud data. This scheme allows multiple keyword in the search request and returns the documents in the order of their relevance to these keywords. A fuzzy keyword set is built from a predefined set of words based on edit distance so that it can return the matching files or closest possible matching files. Here coordinate matching is used based on secure inner product computation to measure similarity. The proposed scheme is secure and privacy preserving while introducing low overhead on computation and communication.
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Provide Security of Ranked Multi keyword Search over
Encrypted Data in cloud computing

Provide Security of Ranked Multi keyword Search over Encrypted Data in cloud computing

In order to satisfy the sensible search necessities, search over encrypted information ought to support the subsequent 3 functions. First, the searchable encoding schemes ought to support multi-keyword search, and supply constant user expertise as looking out in Google search with completely different keywords; single-keyword search is much from satisfactory by solely returning terribly restricted and inaccurate search results. Second, to quickly establish most relevant results, the search user would generally like cloud servers to type the came search leads to a relevancy-based order [14] graded by the relevance of the search request to the documents. additionally, showing the graded search to users may eliminate the unessential network traffic by solely causing back the foremost relevant results from cloud to go looking users. Third, as for the search potency, since the amount of the documents contained during a information may well be terribly massive, searchable encoding schemes ought to be economical to quickly answer the search requests with minimum delays.
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Privacy on Encrypted Cloud Data with Multi – Keyword Ranked Search

Privacy on Encrypted Cloud Data with Multi – Keyword Ranked Search

The search query is also described as a binary vector 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 multi keyword 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 MRSE schemes in a step-by-step manner to achieve various stringent privacy requirements.
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Multi Keyword Ranked Search Enabled Encryption on Cloud Data

Multi Keyword Ranked Search Enabled Encryption on Cloud Data

In this paper, the scheme named multi keyword ranked search (MKRS) enabled encryption on cloud data is specified and resolved. In the several semantics available for multi keyword searching “co-ordinate matching” is used here. When the index construction is carried out, 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 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. But outsourcing of the data vector or the query vector directly will violate the index privacy or the search privacy. For taking on challenges of supporting such multikeyword semantic without privacy breaches, a basic idea for the MKRS using secure inner product computation is considered. This is adapted from secure k-nearest neighbour (kNN) technique [5].
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MULTI-KEYWORD RANKED SEARCH MODEL FOR ENCRYPTED CLOUD DATA

MULTI-KEYWORD RANKED SEARCH MODEL FOR ENCRYPTED CLOUD DATA

In the proposed scheme , the data owner is responsible for updating the information and sending them to cloud server. It could be a meaningful but difficult future work to design a dynamic searchable encryption scheme whose updating operation can be completed by cloud server only. Actually In multi-user scheme many secure challenges will be there. Firstly, all the users usually keep the same secure key for trapdoor generation in a symmetric SE scheme. If it is needed to revoke a user in this scheme, we need to rebuild the index and distribute the new secure keys to all the authorized users. Secondly SE schemes usually assume that all the data users are trustworthy. In the future works , we will try to improve the SE scheme to handle these challenging problems.
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MULTI KEYWORD SECURED RANKING FOR AN ENCRYPTED CLOUD DATA

MULTI KEYWORD SECURED RANKING FOR AN ENCRYPTED CLOUD DATA

In general, Cloud computing has become fame in the society. In the cloud computing the main advantage is that more and more data owners centralized their sensitive data in the cloud. Here in the cloud computing there are many and more amount of data files that are stored in the cloud server, and it is an important to provide the keyword based search service to the data user.Nonetheless, with a specific in order to ensure the data security, delicate information is typically encoded before outsourced to the cloud server, which makes the search technology on plaintext unusable. In this paper, we propose a semantic multi-keyword ranked search conspire over the encrypted cloud data, which all the while meets an arrangement of strict privacy requirement. Firstly, we use the "Latent Semantic Analysis" to reveal relationship between terms and documents. The Latent semantic Analysis exploits verifiable implicit higher-order structure in the relationship of terms with documents ("semantic structure") and receives a reduced-dimension vector space to speak to words and reports. Accordingly, the relationship between terms is automatically caught. Besides, our plan utilize secure "k-nearest neighbour (k-NN)" to accomplish secure search functionality. The proposed plan could return the precise coordinating records, as well as the documents including the terms idle semantically related to the inquiry essential word. At last, the experimental result shows that our strategy is superior to the first MRSE plan.
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A Muilt Keyword Ranked Based Search and Privacy Preservation of Distributed Documents in the Network

A Muilt Keyword Ranked Based Search and Privacy Preservation of Distributed Documents in the Network

In the PPI framework, it is alluring to give separated privacy protection with respect to various search queries and proprietors. The information demonstrates utilized as a part of a PPI framework and a data system is that every server has different records, each comprising numerous terms. What is esteemed private and ought to be secured by a PPI is the ownership data as "whether a proprietor has no less than one record significant to a multi-term express." Under this model, the importance of separated privacy conservation is of two folds: 1) Different (single) terms are not conceived rise to as far as how touchy they are. For instance, in an eHealthcare organize, it is normal for a lady to think of her as therapeutic record of a "premature birth" operation to be substantially more delicate than that of a "hack" treatment. 2) A multi-term state, as a semantic unit, can be a great deal increasingly (or less) delicate than a solitary term contained in the expression. For example, "content" and "driving" are two terms that might be regarded non-delicate in their lone appearances; however a record of "content driving" can be viewed as more touchy. The current PPI work [9], [10], [11], while intended to ensure privacy, is not ready to separate privacy conservation on various terms. Because of the quality-rationalist strategies utilized for developing these PPIs, they cannot convey a quantitative certification for privacy safeguarding
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A Secure Dynamic Multi Keyword Ranked Search in Encrypted Cloud Environment

A Secure Dynamic Multi Keyword Ranked Search in Encrypted Cloud Environment

We propose a powerful framework where any approved client can do a pursuit on scrambled information with multiple keywords, without uncovering the keywords he looks for, nor the information of the records that match by the question. Approved clients can make seek forms by distinct keywords on the cloud to recover the pertinent reports. Our proposition framework encourages that a gathering of clients can inquiry the database gave that they have purported trapdoors for the hunt terms that approve the clients to incorporate them in their inquiries. Our proposed framework can play out multiple keyword hunts in a solitary question and positions the outcomes so the client can recover just the most important matches in a requested way. Also, we build up an arrangement of strict protection prerequisites. Among various multi keyword semantics, we select the viable rule of "organize coordinating".
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A Secure and Dynamic Multi-keyword Ranked Search Scheme over Encrypted Cloud Data

A Secure and Dynamic Multi-keyword Ranked Search Scheme over Encrypted Cloud Data

•The secure kNN figuring is utilized to encode the record and question vectors, and after that certification correct congruity score tally between mixed document and request vectors. •To restrict particular ambushes in different risk models, we create two secure interest plots: the fundamental dynamic multi-catchphrase situated look for (BDMRS) scheme in the known ciphertext illustrate, and the updated dynamic multikeyword situated look (EDMRS) plot in the known establishment show. Central indicates Due the phenomenal structure of our tree-based document, the proposed look design can adaptably achieve sub-coordinate chase time and deal with the deletion and expansion of reports We layout an open encryption plot that support both the exact multi-catchphrase situated look for and versatile dynamic assignment on file gathering. Due to the exceptional structure of our tree-based document, the request versatile nature of the proposed plot is for the most part kept to logarithmic. Additionally, essentially, the proposed plan can achieve higher request efficiency by executing our "Anxious Depth- first Search" count. Also, parallel request can be adaptably performed to moreover diminish the time cost of chase process.
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A Novel Approach for Secure Ranked Multi-Keyword Search Using Multiple Data Owners in Cloud Computing

A Novel Approach for Secure Ranked Multi-Keyword Search Using Multiple Data Owners in Cloud Computing

It define and solve the problem of multi- keyword ranked search over encrypted cloud data while preserving strict system wise privacy in the cloud computing paradigm. Among various multi-keyword semantics, To 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, To 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.
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A Survey on Secure and Aggressive Multi-Keyword Ranked Search Over Encrypted Cloud Data

A Survey on Secure and Aggressive Multi-Keyword Ranked Search Over Encrypted Cloud Data

Zhihua Xia,Xinhui Wang, Xingming Sunand Qian Wang, fellow, IEEE [1]in this paper they work on a secure tree- based search scheme over the encrypted cloud storage, which support multi keyword ranked search across with dynamic operation on document collection available at server. The vector space model and term frequency (TF) × inverse document frequency (IDF) model are combinly used in the creation of index and generation of query to provide multi keyword ranked search output. To get high search efficiency results, author construct a tree-based index structure and proposed a Greedy Depth-first Search algorithm based on this index tree. Because of this special structure of tree- based index, the proposed search scheme can flexibly reach sub linear search time and can effectively deal with the deletion and insertion of documents. The kNN algorithm is applied to encrypt the index and query vectors, and till then make sure accurate relevance score calculation between encrypted index and query vectors. The proposed scheme is designed to provide not only multi-keyword query and correct result ranking, but also dynamic update on document collections. They describe the unencrypted dynamic multi-keyword ranked search (UDMRS) scheme which is construct on the basis of vector space model and KBB tree. Based on the UDMRS scheme, two secure search schemes (BDMRS and EDMRS schemes) are constructed against two threat models, respectively. There are still many challenges in symmetric SE schemes. In the proposed scheme, the data owner is responsible for generating updating information and sending them to the cloud server. Thus, the data owner requires storing the unencrypted index tree and the information that are necessary to recalculate the IDF values. Such an active data owner may not be very suitable for the cloud computing model. It could be an important but difficult future work to design a dynamic searchable encryption scheme whose updating operation can be completed by cloud server only, for now reserving the ability to support multi-keyword ranked search. In addition, as the most of works about searchable encryption, this scheme mainly considers the challenge from the cloud server.
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Multi-Keyword Ranked Search over Encrypted Cloud Data with Multiple Data Owners

Multi-Keyword Ranked Search over Encrypted Cloud Data with Multiple Data Owners

Secure search over encrypted cloud data reduces the computation and storage cost. Secure ranked multi-keyword search, fuzzy keyword search, similarity search all these searches are also performed on encrypted cloud data. Data user authentication technique, Different-key encrypted keywords matching and privacy preserving ranked search of files methods are used to solve the problem of secure multi-keyword search for multiple data owners and multiple data users in cloud computing environment. When huge amount of data owners [3], [9] are involved then they generate trapdoors simultaneously which affect the flexibility and usability of search system.
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