Multi-keyword Ranked Search Research Articles

Secure Multi-keyword Ranked Search over Encrypted Cloud Data

Secure Multi-keyword Ranked Search over Encrypted Cloud Data

Searching Encrypted Data Over Cloud

Cloud is one of the type of application which can be used by using Internet for storing, inserting and managing the data in very less cost. By considering the benefits and advantages of Cloud storage server users can manage their data in well organizations which is outsourced on the Cloud in Encrypted format to secure the data. Cloud computing is the technology that uses the internet to manage data and applications cloud computing helps data users to outsource their data to the Cloud instead of using different hardware and software to manage the data. Generally keyword based searching approach is used and this is not efficient technique due to this single keyword searching technique is used. A Greedy Depth First Search is used for achieving efficient searching in the large database. The KNN algorithm is used for protecting the data on the Cloud from different attacks. The AES algorithm is utilized encryption and decryption purpose of multimedia files. KeywordSearchable encryption, multi-keyword ranked search, dynamic update, cloud computing.

Enabling Personalized Search over Encrypted Outsourced Data with Efficiency Improvement

In cloud computing, searchable encryption scheme over outsourced data is a hot research field. However, most existing works on encrypted search over outsourced cloud data follow the model of “one size fits all” and ignore personalized search intention. Moreover, most of them support only exact keyword search, which greatly affects data usability and user experience. So how to design a searchable encryption scheme that supports personalized search and improves user search experience remains a very challenging task. In this paper, for the first time, we study and solve the problem of personalized multi-keyword ranked search over encrypted data (PRSE) while preserving privacy in cloud computing. With the help of semantic ontology WordNet, we build a user interest model for individual user by analyzing the user’s search history, and adopt a scoring mechanism to express user interest smartly. To address the limitations of the model of “one size fit all” and keyword exact search, we propose two PRSE schemes for different search intentions. Extensive experiments on real-world dataset validate our analysis and show that our proposed solution is very efficient and effective.

A Multi-keyword Ranked Search over Encrypted Cloud Data Supporting Semantic Extension

With the emergence of cloud computing, many data owners outsource their local data to cloud server so as to enjoy high-quality data storage services. For the protection of data privacy, sensitive data has to be encrypted before outsourcing, which makes effective data utilization a challenging task. Although existing searchable encryption technologies enable data users to conduct secure search over encrypted data, the functionality of these schemes need to be further improved. In this paper, we construct a secure and efficient multi-keyword ranked search scheme which supports both the semantic extension search and the multi-keyword ranked search. The semantic extension is achieved through the mutual information statistical analysis of keywords. And the multi-keyword ranked search is achieved through a balanced binary tree whose nodes are the vectors of term frequency (TF) values. The splitting operation and secure transformation are utilized to encrypt the vectors of index and query. Note that, the encrypted vectors can be well used to calculate accurate relevance scores. Phantom terms are added to the index vector to blind the search results to resist statistical attacks. Due to the use of tree-based index structure, the proposed scheme can achieve the sub-linear search time. Finally, the experiments are conducted to demonstrate the efficiency of the proposed scheme.

Safe Multi Keyword Ranked Search over Encrypted Cloud Data

To get more flexibility, data owner interested to outsource their complex data set on the cloud. Cloud computing provides data outsourcing and data high quality service. For data security, the data owner provides encryption on their data. Hence, for Low cost and low computational overhead the data owner migrates their data on the cloud. In subsisting system, for the first instance, the conundrum of privacypreserving multi-keyword ranked search over encrypted data in cloud computing (MRSE) is define and solve. The basic idea of MRSE mechanism is to perform a safe inner product calculation, and then gives two appreciably improved MRSE schemes (Coordinate matching and Inner product similarity) to achieve various privacy requirements in two different models. Advance tree based index structure and numerous adaptive approaches for multi-dimensional (MD) algorithm are propose to increase the search efficiency so that the technical search efficiency is more than that of linear search. General Terms Multi keyword Rank search, MD Algorithm, vector Space Model.

Privacy Preserving Ranked Multi-Keyword Search for Multiple Data Owners in Cloud Computing

With the advent of cloud computing, it has become increasingly popular for data owners to outsource their data to public cloud servers while allowing data users to retrieve this data. For privacy concerns, secure searches over encrypted cloud data has motivated several research works under the single owner model. However, most cloud servers in practice do not just serve one owner; instead, they support multiple owners to share the benefits brought by cloud computing. In this paper, we propose schemes to deal with privacy preserving ranked multi-keyword search in a multi-owner model (PRMSM). To enable cloud servers to perform secure search without knowing the actual data of both keywords and trapdoors, we systematically construct a novel secure search protocol. To rank the search results and preserve the privacy of relevance scores between keywords and files, we propose a novel additive order and privacy preserving function family. To prevent the attackers from eavesdropping secret keys and pretending to be legal data users submitting searches, we propose a novel dynamic secret key generation protocol and a new data user authentication protocol. Furthermore, PRMSM supports efficient data user revocation. Extensive experiments on real-world datasets confirm the efficacy and efficiency of PRMSM.

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

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 $\;\times\;$ 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.

MULTI KEYWORD RANKED SEARCH OVER ENCRYPTED CLOUD DATA

with the initiation of cloud computing, data possessors are driven to contract out their Complex data management systems from local sites to the commercial community cloud for great suppleness and financial savings. But for defending data privacy, complex data have to be encrypted before outsourcing, which obsoletes traditional data use based on plaintext keyword search. Thus, enabling an encrypted cloud data search service is of supreme position. 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 significance to these keywords. Related works on searchable encryption focus on single keyword search or Boolean keyword search, and rarely sort the Search results. We describe and crack the stimulating problem of privacy-preserving multi-keyword ranked search over encrypted data in cloud computing (MRSE).

Confidentiality-conserving Multi-keyword Ranked Search Above Encrypted Cloud Data

In cloud computing, data owners outsource their complex data management systems from local sites to the public cloud for great flexibility and cost-effective. But before outsourcing, sensitive data have to be encrypted for protecting data privacy. This obsoletes traditional data utilization based on plaintext keyword search. Thus, allowing an encrypted cloud data search service is of vital importance. As 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. In literature of searchable encryption single keyword search or Boolean keyword search techniques are discussed. In this paper, we propose the problem of privacy-preserving multi-keyword ranked search over encrypted data in cloud computing (MRSE). In which the queries are transferred to the server. Server searches the relevant content by using the coordinate matching and sends the results to the user. Then the user decrypts the data.

Efficient Multi-keyword Ranked Search over Outsourced Cloud Data based on Homomorphic Encryption

With the development of cloud computing, more and more data owners are motivated to outsource their data to the cloud server for great flexibility and less saving expenditure. Because the security of outsourced data must be guaranteed, some encryption methods should be used which obsoletes traditional data utilization based on plaintext, e.g. keyword search. To solve the search of encrypted data, some schemes were proposed to solve the search of encrypted data, e.g. top-k single or multiple keywords retrieval. However, the efficiency of these proposed schemes is not high enough to be impractical in the cloud computing. In this paper, we propose a new scheme based on homomorphic encryption to solve this challenging problem of privacy-preserving efficient multi-keyword ranked search over outsourced cloud data. In our scheme, the inner product is adopted to measure the relevance scores and the technique of relevance feedback is used to reflect the search preference of the data users. Security analysis shows that the proposed scheme can meet strict privacy requirements for such a secure cloud data utilization system. Performance evaluation demonstrates that the proposed scheme can achieve low overhead on both computation and communication.

Comparison of Privacy Preserving Single-Keyword Search and Multi-Keyword Ranked Search Techniques over Encrypted Cloud Data

Comparison of Privacy Preserving Single-Keyword Search and Multi-Keyword Ranked Search Techniques over Encrypted Cloud Data

English

English

Multi-Keyword Search Over Encrypted Cloud Data Without Violation of Privacy

With the advent of Cloud Computing, Data Owners are motivated to outsource their Data to public cloud for great flexibility and economic savings. But only single keyword or Boolean keyword search is available. So it gives undifferentiated results and often consumes more data bandwidth and time. In this paper, we define and solve the problem of multi-keyword ranked search over encrypted cloud data while preserving strict system wise privacy and eliminating unnecessary network traffic by sending back only the most relevant data by using coordinate matching. Data Owner uploads their Data along with the Index file which comprises of a set of keywords to improve retrieval accuracy; the search result is ranked by the cloud server before relaying it to the data user’s request.

Enabling Efficient Multi-Keyword Ranked Search Over Encrypted Mobile Cloud Data Through Blind Storage

In mobile cloud computing, a fundamental application is to outsource the mobile data to external cloud servers for scalable data storage. The outsourced data, however, need to be encrypted due to the privacy and confidentiality concerns of their owner. This results in the distinguished difficulties on the accurate search over the encrypted mobile cloud data. To tackle this issue, in this paper, we develop the searchable encryption for multi-keyword ranked search over the storage data. Specifically, by considering the large number of outsourced documents (data) in the cloud, we utilize the relevance score and k-nearest neighbor techniques to develop an efficient multi-keyword search scheme that can return the ranked search results based on the accuracy. Within this framework, we leverage an efficient index to further improve the search efficiency, and adopt the blind storage system to conceal access pattern of the search user. Security analysis demonstrates that our scheme can achieve confidentiality of documents and index, trapdoor privacy, trapdoor unlinkability, and concealing access pattern of the search user. Finally, using extensive simulations, we show that our proposal can achieve much improved efficiency in terms of search functionality and search time compared with the existing proposals.

Multi-keyword ranked searchable public-key encryption

Most related works on Public-Key Encryption with Keyword Search scheme also called Searchable Public-key Encryption focus on single keyword search. Some works supporting multi-keyword search cannot provide search result ranking. In this paper, we study the problem of multi-keyword ranked search over encrypted data in public-key model. We propose the definition of Multi-Keyword Ranked Searchable Public-key Encryption/Public-Key Encryption with Multi-Keyword Ranked Search. In the definition, we allow the receiver to query any set of keywords. As the query result, the receiver can get the number of queried keywords appearing in the encrypted data, which can be used to evaluate the similarity ranking of the data keywords to the search query in 'as many matches as possible' principle. We put forward a generic construction for public-key encryption with multi-keyword ranked search scheme with sub-linear ciphertext based on any anonymous hierarchical identity-based encryption scheme and provide the security analysis. At last, we present a concrete scheme with sub-linear ciphertext.

Privacy-preserving Multi-keyword Ranked Search over Encrypted Cloud Data Supporting Dynamic Update

With the development of cloud computing, the sensitive information of outsourced data is at risk of unauthorized accesses. To protect data privacy, the sensitive data should be encrypted by the data owner before outsourcing, which makes the traditional and efficient plaintext keyword search technique useless. Hence, it is an especially important thing to explore secure encrypted cloud data search service. Considering the huge number of outsourced data, there are three problems we are focused on to enable efficient search service: multi-keyword search, result relevance ranking and dynamic update. In this paper, we propose a practically efficient and flexible searchable encrypted scheme which supports both multi-keyword ranked search and dynamic update. To support multi-keyword search and result relevance ranking, we adopt Vector Space Model (VSM) to build the searchable index to achieve accurate search result. To improve search efficiency, we design a tree-based index structure which supports insertion and deletion update well without privacy leakage. We propose a secure search scheme to meet the privacy requirements in the threat model. Finally, experiments on real-world dataset are implemented to demonstrate the overall performance of the proposed scheme, which show our scheme is efficient.

Achieving effective cloud search services: multi-keyword ranked search over encrypted cloud data supporting synonym query

In recent years, consumer-centric cloud computing paradigm has emerged as the development of smart electronic devices combined with the emerging cloud computing technologies. A variety of cloud services are delivered to the consumers with the premise that an effective and efficient cloud search service is achieved. For consumers, they want to find the most relevant products or data, which is highly desirable in the pay-as-you use cloud computing paradigm. As sensitive data (such as photo albums, emails, personal health records, financial records, etc.) are encrypted before outsourcing to cloud, traditional keyword search techniques are useless. Meanwhile, existing search approaches over encrypted cloud data support only exact or fuzzy keyword search, but not semantics-based multi-keyword ranked search. Therefore, how to enable an effective searchable system with support of ranked search remains a very challenging problem. This paper proposes an effective approach to solve the problem of multi-keyword ranked search over encrypted cloud data supporting synonym queries. The main contribution of this paper is summarized in two aspects: multi-keyword ranked search to achieve more accurate search results and synonym-based search to support synonym queries. Extensive experiments on real-world dataset were performed to validate the approach, showing that the proposed solution is very effective and efficient for multikeyword ranked searching in a cloud environment.

Dynamic Multi-keyword Top-k Ranked Search over Encrypted Cloud Data

Nowadays, more and more people are motivated to outsource their local data to public cloud servers for great convenience and reduced costs in data management. But in consideration of privacy issues, sensitive data should be encrypted before outsourcing, which obsoletes traditional data utilization like keyword-based document retrieval. In this paper, we present a secure and efficient multi-keyword ranked search scheme over encrypted data, which additionally supports dynamic update operations like deletion and insertion of documents. Specifically, we construct an index tree based on vector space model to provide multi-keyword search, which meanwhile supports flexible update operations. Besides, cosine similarity measure is utilized to support accurate ranking for search result. To improve search efficiency, we further propose a search algorithm based on “Greedy Depth-first Traverse Strategy”. Moreover, to protect the search privacy, we propose a secure scheme to meet various privacy requirements in the known ciphertext threat model. Experiments on the real-word dataset show the effectiveness and efficiency of proposed scheme.

Privacy-Preserving Multi-Keyword Ranked Search over Encrypted Cloud Data

With the advent of cloud computing, 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 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 product similarity to quantitatively evaluate such 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.

Efficient multi-keyword ranked query over encrypted data in cloud computing

Cloud computing infrastructure is a promising new technology and greatly accelerates the development of large scale data storage, processing and distribution. However, security and privacy become major concerns when data owners outsource their private data onto public cloud servers that are not within their trusted management domains. To avoid information leakage, sensitive data have to be encrypted before uploading onto the cloud servers, which makes it a big challenge to support efficient keyword-based queries and rank the matching results on the encrypted data. Most current works only consider single keyword queries without appropriate ranking schemes. In the current multi-keyword ranked search approach, the keyword dictionary is static and cannot be extended easily when the number of keywords increases. Furthermore, it does not take the user behavior and keyword access frequency into account. For the query matching result which contains a large number of documents, the out-of-order ranking problem may occur. This makes it hard for the data consumer to find the subset that is most likely satisfying its requirements. In this paper, we propose a flexible multi-keyword query scheme, called MKQE to address the aforementioned drawbacks. MKQE greatly reduces the maintenance overhead during the keyword dictionary expansion. It takes keyword weights and user access history into consideration when generating the query result. Therefore, the documents that have higher access frequencies and that match closer to the users’ access history get higher rankings in the matching result set. Our experiments show that MKQE presents superior performance over the current solutions.