Top PDF An Efficient Storage and Retrieval Method Based On Multi Key Ranked Search & Improved Hierarchical Clustering Index for Cloud Data

An Efficient Storage and Retrieval Method Based On Multi Key Ranked Search & Improved Hierarchical Clustering Index for Cloud Data

An Efficient Storage and Retrieval Method Based On Multi Key Ranked Search & Improved Hierarchical Clustering Index for Cloud Data

In this work, we are presenting an efficient data storage and retrieval method for encrypted data based on multi- keyword ranked search by improved hierarchical clustering index for cloud data to improve cloud performance. The proposed method basically takes place in two phases. In first phase IAES-256 bit data encryption and decryption methods are used to maintain data security and SHA-1 method is used to calculate the hash values of the message to maintain the data integrity and second phase uses efficient data retrieval method based on multi-keyword ranked search by improved hierarchical clustering index (by dynamic K-mean) with bagging approach for cloud data. Bagging provides a predictive probabilistic model which reduces the noise and irrelevant data during classification, which improves the accuracy. The proposed method uses an “In order” to verify the authenticity of search results, a structure called minimum hash sub-tree.
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A Secure and   multi-keyword ranked search scheme over encrypted based on hierarchical clustering index

A Secure and multi-keyword ranked search scheme over encrypted based on hierarchical clustering index

filters. Bosch et al worked on the concept given by Goh et al. and introduced the concept of wild card searches. The drawback of this scheme is that bloom filters may introduce false positives. In Chang’s et al proposed scheme, an index is built for each document. The scheme is more secured compared to Goh’s scheme since number of words in a file is not disclosed. The limitation of this scheme is that it is less efficient and does not support arbitrary updates with new words. Golle et al scheme allows multiple keyword searches with one encrypted query. But this scheme is not practical. Curtmola et al for the first time proposed the concept of symmetric searchable encryption (SSE), later on Kamara et al proposed an extended version of SSE called dynamic SSE (DSSE), where addition and deletion of documents can be performed in index table. All these schemes are based on single keyword search [22] [23] [24]. The first public key encryption with keyword search (PEKS) was proposed by Boneh et al. The scheme suffers from inference attack on trapdoor encryption method. Baek et al, Rhee et al improved hardness of security of Boneh’s scheme. Baek’s scheme introduces the concept of conjunction of keyword search. The public key encryption methods are computationally time consuming and complex that makes these algorithms inefficient. In Yang et al scheme the encrypted data is searched by individual users using a unique key allotted to them. The scheme
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Compact Multi-keyword Ranked Scrutiny Scheme using Cryptographic Method in Cloud Data

Compact Multi-keyword Ranked Scrutiny Scheme using Cryptographic Method in Cloud Data

Abstract: Cloud computing is modern technology as a new computing model in number of business domains. Large numbers of large scale departments are startingto shift the data on to the cloud environment. With the advantage of storage as a service many enterprises are moving their valuable data to the cloud, since it costs less, easily scalable and can be accessed from anywhere any time. Improved dynamic multi-keyword ranking search scheme with top key via encrypted cloud data that simultaneously supports dynamic update operations as deleting and inserting documents. Greedy depth first search algorithm is provided for efficiency multi keywords on place and index structure. Cryptography is one of the establishing trust models. Searchable security is a cryptographic method to provide security. In number of researchers have been working on developing security and efficient searchable encryption methods. We take new effective cryptographic techniques based on data structures like CRSA and B-Tree to enhance the level of security. We propose new multi-keyword search query over encrypted cloud data in retrieving top k scored documents.The vector space model and TFIDF model are used to construct index and query generation. This paper focuses on multi keyword search based on ranking over an encrypted cloud data (MRSE). The search uses the feature of similarity and inner product similarity matching. We propose to support the top-k Multi-full-text search security and performance analysis show that the proposed scheme guarantees a high safety and practicality and dynamic update operations, such as deleting and inserting documents. The experimental results show that the overhead in computation and communication is low.
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ENHANCED SEARCH SCHEME BASED ON EFFICIENT MULTI-KEYWORD RANKED SEARCH IN CLOUD

ENHANCED SEARCH SCHEME BASED ON EFFICIENT MULTI-KEYWORD RANKED SEARCH IN CLOUD

As the popularity of cloud increases, a large number of data owners were motivated for outsourcing the information to the cloud servers which will be convenient for users and also reduction in cost for managing the data. But confidential information has to be encrypted in prior to outsource the data to provide privacy and security that can be used for data retrieval through keyword based scheme. The paper provides a multiple keyword rank search technique on data encrypted that can be used to support dynamic operations such as insertion and deletion of data in document. Widely used models are vector space and TF-IDF were commonly used in combination with the query generation and construction of index. An index structure is constructed on the basis of tree and an algorithm is proposed which is depth-first search tree, a greedy algorithm that helps to provide efficiency in multiple keyword rank search. A secure algorithm called KNN is used for encrypting the query vectors and also the index. The usage of KNN algorithm also provides accurate score computation among the query vectors and indexes which are encrypted. To protect from statistical attacks, apparition words were included in the index to bind with the search result. As a special tree-based indexing structure was implemented, the proposed method will achieve more efficiency in time for search and also provides flexibility for performing insertion and deletion operations in a document. Substantial procedures were used for the demonstration of the proposed scheme for achieving greater efficiency.
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Secure and Efficient Multi-keyword Ranked Search Method on Cloud

Secure and Efficient Multi-keyword Ranked Search Method on Cloud

The extensive use of cloud services has resulted in dramatic growth in volume of data which has made information retrieval much more difficult than before. Even text documents are encrypted before being outsourced to cloud servers. This helps to protect user’s data privacy. Existing techniques to search over encrypted data are not suitable for a huge data environment. Due to the blind encryption, relationship between the documents is concealed which further leads to search accuracy performance degradation. Therefore it is necessary to adopt an approach to support more search semantic and for fast search within enormous data. A hierarchical clustering method for cipher text search is proposed in this paper. The proposed approach clusters the documents based on the minimum similarity threshold, and then partitions the resultant clusters into sub-clusters until the constraint on the maximum size of cluster is reached. The very first thing which cloud server does is that it performs search activity and then selects the k documents (previously decided by the user and sent to the cloud server) from the minimum desired sub-category. To request desired documents, instead of using single keyword search query, multi-keyword search technique is proposed.
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Searching, Ranking & Downloading The Dynamic Multi-Keyword Ranked Based Encrypted Cloud Data

Searching, Ranking & Downloading The Dynamic Multi-Keyword Ranked Based Encrypted Cloud Data

ABSTRACT: Software protection is one of the most important issues concerning computer practice. There exist many heuristics and ad-hoc methods for protection, but the problem as a whole has not received the theoretical treatment it deserves. In this paper, we provide theoretical treatment of software protection. We reduce the problem of software protection to the problem of efficient simulation on oblivious RAM.A machine is oblivious if the sequence in which it accesses memory locations is equivalent for any two inputs with the same running time. For example, an oblivious Turing Machine is one for which the movement of the heads on the tapes is identical for each computation. (Thus, the movement is independent of the actual input.) What is the slowdown in the running time of a machine, if it is required to be oblivious? In 1979, Pippenger and Fischer showed how a two-tape oblivious Turing Machine can simulate, on-line, a one-tape Turing Machine, with a logarithmic slowdown in the running time. We show an analogous result for the random-access machine (RAM) model of computation. In particular, we show how to do an on-line simulation of an arbitrary RAM by a probabilistic oblivious RAM with a poly logaithmic slowdown in the running time. On the other hand, we show that a logarithmic slowdown is a lower bound.
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An Effective Multi-Keyword Ranked Search Procedure over Encrypted Cloud Data with Enhanced Protection

An Effective Multi-Keyword Ranked Search Procedure over Encrypted Cloud Data with Enhanced Protection

huge cost in terms of data usability. For example, the existing techniques on keyword-based information retrieval, which are widely used on the plaintext data, cannot be directly applied on the encrypted data. Downloading all the data from the cloud and decrypt locally is obviously impractical. In order to address the above problem, researchers have designed some general-purpose solutions with fully-homomorphic encryption [3] or oblivious RAMs [4]. However, these methods are not practical due to their high computational overhead for both the cloud sever and user. On the contrary, more practical specialpurpose solutions, such as searchable encryption (SE) schemes have made specific contributions in terms of efficiency, functionality and security. Searchable encryption schemes enable the client to store the encrypted data to the cloud and execute keyword search over ciphertext domain. So far, abundant works have been proposed under different threat models to achieve various search functionality, such as single keyword search, similarity search, multi-keyword boolean search, ranked search, multi- keyword ranked search, etc. Among them, multi keyword ranked search achieves more and more attention for its practical applicability. Recently, some dynamic schemes have been proposed to support inserting and deleting operations on document collection. These are significant works as it is highly possible that the data owners need to update their data on the cloud server. But few of the dynamic schemes support efficient multi keyword ranked search.
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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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Title: A Review Paper on Implementation of a Secure and Dynamic Multi-Keyword Ranked Search Scheme over Encrypted Cloud Data

Title: A Review Paper on Implementation of a Secure and Dynamic Multi-Keyword Ranked Search Scheme over Encrypted Cloud Data

Abstract— The major aim of this paper is to solve the problem of multi-keyword ranked search over encrypted cloud data at the time of protecting exact method wise privacy in the cloud computing concept in this paper we are using three main parts first one is Data Owner, second one is Data User and third one is cloud computing. In this paper we introduce Implementation of a secure multi-Keyword Ranked search scheme over encrypted cloud data. Specifically, the vector space model and the broadly utilized TF×IDF model are joined as a part of the record development and query generation. We build a unique tree-based file structure and propose "greedy Depth- first Search" calculation to give efficient multi-keyword ranked search with no. of count matching multi-keywords. The protected KNN calculation is used to encrypt the file and query vector. The vector space model facilitates to offer enough search accuracy and the Triple DES encryption used to provide security.
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An Efficient and Privacy Preserving in Multi-          Keyword Ranked Search over Encrypted Cloud Data

An Efficient and Privacy Preserving in Multi- Keyword Ranked Search over Encrypted Cloud Data

Abstract— Innovation in cloud computing have revamped the view of modern information technology which is motivates the data owners to outsource the data to public cloud server for fast access to data its management at minimum cost. Earlier it was not possible to upload the encrypted data over the cloud. Now-a- days with the increasing number of users the data is also increasing, so there is need to provide security to data over the cloud. To provide security the document should be first encrypted before outsourcing it and it can be retrieved effectively. Some more additional features can be provided along with search such as dynamic update operations. For the index creation and query generation the vector space model and the TF IDF model are combined. A tree-based index structure is constructed and a GDFS algorithm is proposed to produce efficient multi-keyword ranked search. kNN algorithm is used for encrypting the index and query vectors, to acquire accuracy of relevance score between encrypted index and query vectors. To avoid statistical attacks, phantom terms are included to index vector for building the search results.
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An Efficient Ranked Multi-Keyword Search for Multiple Data Owners over Encrypted Cloud Data

An Efficient Ranked Multi-Keyword Search for Multiple Data Owners over Encrypted Cloud Data

A Searchable symmetric encryption (SSE) system is enables a gathering to outsource the capacity of his information to another gathering private, while keeping up the capacity to specifically look over it. The concentration of dynamic research and a few security definitions this issue are occurred. In this framework we propose new and more grounded security definitions. We permit two manifestations that we permit secure under our new definitions. With fulfilling more grounded security guarantees, and this is more proficient than every past development. In new framework chip away at SSE just considered the setting where just the proprietor of the information is equipped for submitting seek questions. We consider the normal expansion where a discretionary gathering of gatherings other than the proprietor can submit look inquiries. We formally characterize SSE in this multi-client setting, and present a productive development
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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

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

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

overhead [1]. Attracted by these appealing features, both individuals and enterprises are motivated to outsource their data to the cloud, instead of purchasing software and hardware to manage the data themselves. Despite of the various advantages of cloud services, outsourcing sensitive information (such as e-mails, personal health records, company finance data, government documents, etc.) to remote servers brings privacy concerns. The cloud service providers (CSPs) that keep the data for users may access users’ sensitive information without authorization. A general approach to protect the data confidentiality is to encrypt the data before outsourcing [2]. However, this will cause a huge cost in terms of data usability. For example, the existing techniques on keyword-based information retrieval, which are widely used on the plaintext data, cannot be directly applied on the encrypted data. Downloading all the data from the cloud and decrypt locally is obviously impractical. In order to address the above problem, researchers have designed some general-purpose solutions with fully-homomorphic encryption [3] or oblivious RAMs[4]. However, these methods are not practical due to their high
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Keyword Based Search Outcomes with Ranked Verification In Cloud Storage

Keyword Based Search Outcomes with Ranked Verification In Cloud Storage

With the approach of cloud computing, an increasing number of persons have a tendency to outsource their information to the cloud. As a key information use, secure keyword search over encoded cloud information has intent the concern of more investigators. But, many of existing researchers depend on a perfect belief that the cloud server is "interested however legit", where the searched lists are most certainly not tested. In this paper, we consider moreover a difficult model, where the cloud server would most likely carry on deceitfully. In light of this model, we investigate the issue of result validation for the secure ranked keyword search. Not quite the same as past information confirmation plans, we propose a unique constrained based plan. With our precisely expressed verification information, the cloud server can't know which data owners, or what number of data owners trade handle information which will be utilized for confirming the cloud server's misconduct. With our deliberately planned confirmation development, the cloud server can't know which data owners' information are installed in the validation information buffer, or what number of data owners' searched information are really utilized for validation. All the cloud server identifies that, when he carries on deceptively, he would be found with a high possibility, and rejected genuinely once found. Besides, we propose to upgrade the estimation of parameters utilized as a part of the development of the secret validation information buffer. At last, with intensive investigation and broad analyses, we insist the adequacy and productivity of our proposed plans.
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Multi Keyword Ranked Search Enabled Encryption on Cloud Data

Multi Keyword Ranked Search Enabled Encryption on Cloud Data

The core components of a cryptographic storage service can be implemented using a variety of techniques, some of which were developed specifically for cloud storage. When preparing data for storage in the cloud, the data processor begins by indexing it and encrypting it with a symmetric encryption scheme under a unique key. It then encrypts the index using a searchable encryption scheme and encrypts the unique key with an attribute-based encryption scheme under an appropriate policy. Finally, it encodes the encrypted data and index in such a way that the data verifier can later verify their integrity using a proof of storage. A proof of storage is a protocol executed between a client and a server with which the server can prove to the client that it did not tamper with its data. Proofs of storage can be either privately or publicly verifiable. Privately verifiable proofs of storage only allow the client (i.e., the party that encoded the file) to verify the integrity of the data. With a publicly verifiable proof of storage, on the other hand, anyone that possesses the client‟s public key can verify the data‟s integrity.
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MULTI KEYWORD RANKED SEARCH OVER ENCRYPTED CLOUD DATA

MULTI KEYWORD RANKED SEARCH OVER ENCRYPTED CLOUD DATA

Z.Xia et al., [2015] [9] constructs 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. Experimental results demonstrate the efficiency of our proposed scheme. In the proposed scheme, the data owner is responsible for generate, update information and send to 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. Most of works about searchable encryption, our scheme considers the challenge from the cloud server. Actually, there are many secure challenges in a multi-user scheme.
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Privacy on Encrypted Cloud Data with Multi – Keyword Ranked Search

Privacy on Encrypted Cloud Data with Multi – Keyword Ranked Search

This module includes the user registration login details. This module is used to help the client to search the file using the multiple key words concept and get the accurate result list based on the user query. The user is going to select the required file and register the user details and get activation code in mail email before enter the activation code. After user can download the Zip file and extract that file.

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SECURE RANKED MULTI KEYWORD SEARCH FOR DATA OWNERS IN CLOUD

SECURE RANKED MULTI KEYWORD SEARCH FOR DATA OWNERS IN CLOUD

together keywords besides hatches, we methodically concept a different protected exploration process. As a consequence, dissimilar data proprietors use dissimilar keys to encode their records and keywords. Authentic data employers container issue an enquiry deprived of meaningful clandestine solutions of these dissimilar data proprietors.

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MULTI-KEYWORD RANKED SEARCH MODEL FOR ENCRYPTED CLOUD DATA

MULTI-KEYWORD RANKED SEARCH MODEL FOR ENCRYPTED CLOUD DATA

Now a days the popularity of cloud computing is increasing rapidly, So more data owners are motivated to outsource their data to cloud servers for great convenience and reduced cost in data management. To outsource the sensitive data it should be encrypted for privacy requirements ,which obsoletes data utilization like keyword-based document retrieval. In this paper, to support dynamic update operations like deletion and insertion of documents, we present a "Multi-keyword ranked search model for encrypted cloud data". Ranked search can enable quick search search for the most relevant data , sending back only the top-k most relevant documents can effectively decrease network traffic. Specially the widely used TF-IDF model are combined in index construction and query generation. Due the use of special tree-based index structure "Greedy Depth First search", the proposed scheme can achieve sub-linear search time and deal with deletion and insertion of documents flexibly. The secure KNN algorithm is utilized to encrypt the index and query vectors.
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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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