12,708 research outputs found

    Split keyword fuzzy and synonym search over encrypted cloud data

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    A substitute solution for various organizations of data owners to store their data in the cloud using storage as a service(SaaS). The outsourced sensitive data is encrypted before uploading into the cloud to achieve data privacy. The encrypted data is search based on keywords and retrieve interested files by data user using a lot of traditional Search scheme. Existing search schemes supports exact keyword match or fuzzy keyword search, but synonym based multi-keyword search are not supported. In the real world scenario, cloud users may not know the exact keyword for searching and they might give synonym of the keyword as the input for search instead of exact or fuzzy keyword due to lack of appropriate knowledge of data. In this paper, we describe an efficient search approach for encrypted data called as Split Keyword Fuzzy and Synonym Search (SKFS). Multi-keyword ranked search with accurate keyword and Fuzzy search supports synonym queries are a major contribution of SKFS. The wildcard Technique is used to store the keywords securely within the index tree. Index tree helps to search faster, accurate and low storage cost. Extensive experimental results on real-time data sets shows, the proposed solution is effective and efficient for multi-keyword ranked search and synonym queries Fuzzy based search over encrypted cloud data. © 2017 Springer Science+Business Media, LL

    IGSK: Index Generation on Split Keyword for search over cloud data

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    Storage as a Service (Saas) of cloud computing has become an alternative option for data owners of various organizations to store their data into the cloud. Usually sensitive data is encrypted to achieve data security and then it is outsourced into cloud. Many traditional search schemes allow data user to search over encrypted cloud data through keywords and retrieve the files of interest selectively. In this paper, we propose an efficient approach for keyword search over encrypted cloud data. The main contribution of this paper involves index generation method for keywords by using split factor. The keywords are stored in wildcard based technique within the index tree that is stored securely with low storage cost. Extensive experimental results on real-time data sets shows that the proposed solution is effective as well as efficient in Index generation and storage cost

    Performance Study of Cryptography based Dynamic Multi-Keyword Searchable Security Algorithm in Cloud Using CRSA /B+ Tree

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    Today, Cloud computing is a buzz word in IT industry. Cloud, a shared pool of computing resources, allows access to needed resources on demand through internet and web applications. Since data is outsourced to third party, user needs to maintain the accountability of their data in cloud. Hence preserving the confidentiality and securing the sensitive data in cloud is a major concern. Many cryptographic techniques have been proposed by researchers to assure the confidentiality of the user2019;s data in cloud. But, the challenging task is to provide the secure search over this preserved data which has been encrypted so as to retrieve the effective data. Hence, we are proposing a system to have a secure search over the encrypted data on the cloud which preserves its confidentiality. In our system, a noble approach has been made using the Commutative-RSA algorithm, a cryptographic technique where the dual encryption takes place thus reducing the overall computation overhead. The search operation over the encrypted data is based on the tree search algorithm which supports multi-keyword search. Based on the relevance score, the more appropriate data is retrieved on the search operation. Using this approach, the information is not leaked when the encrypted data is searched by users and also the queries are handled in an efficient way. Finally, we demonstrate the effectiveness and efficiency of the proposed schemes through extensive experimental evaluation

    DYNAMIC SEARCHABLE OVER ENCRYPTED CLOUD DATA FOR MULTI KEYWORD RANKED SEARCH SCHEME

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    As a result of rising status of cloud computing, increasingly more information proprietors tend to be provoked to subcontract their data to cloud machines for huge expediency and cost this is certainly abridged information company. However, responsive information must be encrypted before outsourcing for solitude needs, which obsoletes data operation akin to document retrieval that is keyword-based. In this article, we truth be told there a cramped multi-keyword ranked research method over encrypted cloud data, which simultaneously chains modernize this is certainly lively like removal and insertion of papers. Particularly, the vector space model and also the TF this is certainly widely-used IDF are mutual in the index building and query generation. We produce a certain directory site this is certainly tree-based and recommend a “Greedy Depth-first Search” algorithm to give efficient multi-keyword rated search. The kNN that is secure is useful to encrypt the index and query vectors, and meanwhile guarantee precise value score calculation between encrypted index and query vectors. To be able to withstand attacks which are numerical apparition terms are added to the index vector for blinding search results. As a result of utilize of your certain index this is certainly tree-based, the planned system can realize sub-linear search time and contract with the removal and introduction of documents athletically. Extensive experiments are carried out showing the competence associated with suggested plan

    DYNAMIC MULTI-KEYWORD RANKING SCHEME ON ENCRYPTED CLOUD DATA

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    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 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 time and deal with the deletion and insertion of documents flexibly. Extensive experiments are conducted to demonstrate the efficiency of the proposed scheme

    A New Algorithm to Generate The Integrated Access Trees For A Document Collection

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    ABE schemes have been broadly in use to firmly store and allocate data in cloud computing. A procedure method which congregates the requirements of different data users is considered and used to encrypt distributed file systems. Cloud computing accumulates and categorizes a huge amount of data method possessions to endow with secure, efficient, flexible and on demand services. In ABE schemes, every document is encrypted independently and a data user can decrypt a document if the attribute set equivalents the access formation of the document. In this we confine our consideration to the document collection encryption-decryption process and pay no attention to the other technical challenges such as symmetric encryption algorithms and encrypted document search algorithms. The protection of CP-ABHE is hypothetically established and the efficiency of the integrated access tree construction algorithm is scrutinized in facet. The intact document outsourcing and sharing system contains abundant delve into lines

    Oblivious Substring Search with Updates

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    We are the first to address the problem of efficient oblivious substring search over encrypted data supporting updates. Our two new protocols SA-ORAM and ST-ORAM obliviously search for substrings in an outsourced set of n encrypted strings. Both protocols are efficient, requiring communication complexity that is only poly-logarithmic in n. Compared to a straightforward solution for substring search using recent “oblivious data structures” [30], we demonstrate that our tailored solutions improve communication complexity by a factor of logn. The idea behind SA-ORAM and ST-ORAM is to employ a new, hierarchical ORAM tree structure that takes advantage of data dependency and optimizes the size of ORAM blocks and tree height. Based on oblivious suffix arrays, SA-ORAM targets efficiency, yet does not allow updates to the outsourced set of strings. ST-ORAM, based on oblivious suffix trees, allows updates at the additional communications cost of a factor of loglogn. We implement and benchmark SA-ORAM to show its feasibility for practical deployments: even for huge datasets of 2^40 strings, an oblivious substring search can be performed with only hundreds of KBytes communication cost
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