Multi-keyword Ranked Search Research Articles

A dynamic and verifiable multi-keyword ranked search scheme in the P2P networking environment

With the rapid development of the Internet, Peer to Peer(P2P) network has been applied in various fields. Users in P2P network also have a large amount of data, but users cannot provide enough storage space locally. More and more users choose to upload their own data to cloud server in order to save overhead and facilitate sharing their own data with other users. In order to ensure data security, researchers have proposed searchable encryption(SE) technology, and searchable encryption has been widely used. In this paper, a dynamic verifiable multi-keyword ranked search scheme is proposed under the background of P2P network and cloud storage service(CSS). On the basis of using secure kNN algorithm to encrypt index and traditional inner product algorithm to obtain ranked results, the scheme in this paper realizes forward and backward security by changing the structure of file vector and using modular residual computation. Meanwhile, the integrity and freshness of search results are verified by combining timestamp chain and Merkle tree. Finally, the security of this scheme under two threat models is analyzed, and the performance evaluation experiment is carried out on the document set.

Multi-Keyword ranked search based on mapping set matching in cloud ciphertext storage system

ABSTRACT Most of the existing outsourced encrypted data schemes are retrieved based on the query keyword entered by authorised users. However, with the increase of the data scale in the cloud storage system, the retrieval efficiency of existing solutions has not been significantly improved. In this paper, a multi-keyword ranked search scheme for ciphertext based on mapping set matching (MSMR) is proposed, where (1) The private cloud server matches the keyword numbering set corresponding to the document index vector and the keyword numbering set corresponding to the query vector and sends the document identifier of the matching keyword numbering to the public cloud server. The public cloud server filters the documents irrelevant to the query request according to the document identifier corresponding to the matching keyword numbering, which effectively reduces the time spent in calculating the correlation score, and (2) the document index vector and query vector are segmented before encrypting them out, reducing the time to construct such vectors. Theoretical analysis shows that the proposed scheme is secure in the known ciphertext model. Experimental results confirm that whenever the data scale grows, the improvement of MSMR retrieval efficiency is more significant.

Privacy Preserving Data Mining With Multi Keyword Ranked Search for Medical Data

Privacy Preserving Data Mining (PPDM) maintains the privacy of data stored in cloud. This work aims to protect outsourced data in cloud, and also permit multi keyword search over the encrypted data in a secure way by NLP process without downloading and decrypting all files. Different methods for privacy preservation were analyzed and randomization for multilevel trust is proposed along with an efficient method for keyword search in cloud.

Leakless privacy-preserving multi-keyword ranked search over encrypted cloud data

The Journal of Surveillance, Security and Safety is an international, peer-reviewed, open access journal which provides a forum for the publication of papers addressing the variety of theoretical, methodological, epistemological, empirical and practical issues concerns reflected in the field of information security, cyber security, machine learning, emerging technologies, and their applications.

A Privacy-Preserving Multi-Keyword Ranked Search Over Encrypted Data in Hybrid Clouds

With the rapid development of cloud computing services, more and more individuals and enterprises prefer to outsource their data or computing to clouds. In order to preserve data privacy, the data should be encrypted before outsourcing and it is a challenge to perform searches over encrypted data. In this paper, we propose a privacy-preserving multi-keyword ranked search scheme over encrypted data in hybrid clouds, which is denoted as MRSE-HC. The keyword dictionary of documents is clustered into balanced partitions by a bisecting $k$ -means clustering based keyword partition algorithm. According to the partitions, the keyword partition based bit vectors are adopted for documents and queries which are utilized as the index of searches. The private cloud filters out the candidate documents by the keyword partition based bit vectors, and then the public cloud uses the trapdoor to determine the result in the candidates. On the basis of the MRSE-HC scheme, an enhancement scheme EMRSE-HC is proposed, which adds complete binary pruning tree to further improve search efficiency. The security analysis and performance evaluation show that MRSE-HC and EMRSE-HC are privacy-preserving multi-keyword ranked search schemes for hybrid clouds and outperforms the existing scheme FMRS in terms of search efficiency.

Practical Attribute-Based Multi-Keyword Ranked Search Scheme in Cloud Computing

Attribute-based keyword search (ABKS) has a broad developing prospect in providing search service for users and realizing fine-grained access control over ciphertext in the background of cloud computing. However, two open problems prevent further development and application of ABKS. First, most of ABKS schemes suffer from inside keyword guessing attack (KGA) inherently, which is a great threat to the security of the scheme. Second, the existing ABKS schemes focus on single or conjunctive keyword search, these inflexible retrieval modes may lead to efficiency loss caused by inaccurate positioning of user’s interest and greatly reduce user search experience. In this article, we introduce a semi-trusted server and build a dual server model. Based on the dual server model and our proposed techniques, we are the first to put forward an attribute-based multi-keyword ranked search scheme against inside keyword guessing attack (ABKRS-KGA) to solve the mentioned two problems simultaneously. In our scheme, the queries of users contain weighted keywords and the returned files can be ranked according to user’s query interest. We provide strict security definitions for two types of adversaries and we are the first to prove that the construction is adaptively secure against both chosen-keyword attack (CKA) and KGA. Finally, all-side simulation with real-world data set is implemented for the proposed scheme, and the simulation results show that the efficiency of the proposed scheme is acceptable.

Privacy-Preserving Diverse Keyword Search and Online Pre-Diagnosis in Cloud Computing

With the development of Mobile Healthcare Monitoring Network (MHMN), patients’ data collected by body sensors not only allows patients to monitor their health or make online pre-diagnosis but also enables clinicians to make proper decisions by utilizing data mining technique. However, sensitive data privacy is still a major concern. In this article, we propose practical techniques for searching and making online pre-diagnosis over encrypted data. First, we propose a new Diverse Keyword Searchable Encryption (DKSE) scheme which supports multi-dimension digital vectors range query and textual multi-keyword ranked search to gain a broad range of applications in practice. In addition, a framework called PRIDO based on the DKSE is designed to protect patients’ personal data in data mining and online pre-diagnosis. According to the PRIDO framework, we achieve privacy-preserving naïve Bayesian and decision tree classifiers and discuss its potential applications in actual deployments. Security analysis proves that patients’ data privacy can be well protected without loss of data confidentiality, and performance evaluation demonstrates the efficiency and accuracy in the diverse keyword search, data mining, and disease pre-diagnosis, respectively.

Cross-lingual multi-keyword rank search with semantic extension over encrypted data

The emergence of searchable encryption technology sheds new light on the problem of secure search over encrypted data, which has been a research hotspot in the past few years. However, most existing search schemes based on searchable encryption only support queries in a certain language. The few searchable encryption schemes that have implemented multilingual search fail to achieve automated cross-lingual retrieval, which significantly impacts users search experience. Additionally, most of these schemes only support the exact matching and can-not provide semantic search. Hence, the implementation of cross-lingual and semantic search together remains an open topic for searchable encryption. To the best of our knowledge, no previous research has investigated the problem of cross-lingual ranked search over encrypted cloud data. To address this issue, we propose a cross-lingual multi-keyword rank search (CLRSE) scheme upon the Open Multilingual Wordnet. Our CLRSE scheme can break the barrier of languages, and realizes intelligent and personalized search through flexible keyword and language preference settings. Additionally, an improved scheme is developed to speed up the sorting process. We evaluate the performance of our scheme including security, functionality, precision, and efficiency, with extensive experiments.

Secure Ranked Multi-Keyword Search based on Modified Blowfish Algorithm and AVL Tree in Untrusted Cloud Environment

Advancement in cloud services growing day by day motivates a huge number of data proprietors to store their valuable data in cloud servers. For privacy issue, datasets that contain sensitive information are encrypted first and then outsourced into the cloud server. In this paper, ranked multi keyword search based on AVL tree, especially for several data owners, has been proposed. To avoid unauthorized access, the proposed work encrypts the data using a Modified Blowfish (MB) algorithm before outsourcing. MB algorithm introduces # operation instead of normal OR operation in conventional blowfish algorithm. This MB algorithm provides robustness against any intruding whereas the conventional blowfish algorithm insecure for many applications. To achieve a proficient search, every data owner’s index based on AVL tree is encrypted by way of additive order and the privacy-preserving family is formed. The cloud server is then permitted to combine these indexes effectually without knowing the index content. An Iterative Deepening Depth First Search (IDDFS) procedure is used to discover the matching file for the data user request. Finally, our proposed work provides secure data outsourcing to cloud server compared to the other existing methods. The proposed work considers three metrics for the performance evaluation process, viz. precision, index construction time and search time.

Memory Leakage-Resilient Dynamic and Verifiable Multi-Keyword Ranked Search on Encrypted Smart Body Sensor Network Data

Outsourcing of encrypted smart body sensor network data to the edge or the cloud is now an entrenched practice within organizations, for example to reduce cost and enhance productivity (to some extent), while ensuring data is not accessible to misbehaving cloud service providers. Although there are a large number of searchable symmetric encryption schemes designed to support searching operations on encrypted data, a fully functional and memory leakage-resilient scheme for smart body sensor network data is lacking. This security property is highly desirable as the resource-sharing environment may be prone to various kinds of memory leakage. In this paper, a memory leakage-resilient dynamic and verifiable multi-keyword ranked search scheme (MLR-DVMRS) on encrypted smart body sensor network data is proposed. As each sensor device has inherent characteristics to identify itself, this property can be used to authenticate the device. The proposed scheme utilizes physically unclonable functions and fuzzy extractors to achieve memory leakage-resilience. Meanwhile the vector space model, TF-IDF measure method, and order-preserving encryption, are used to achieve dynamic and multi-keyword ranked search functionalities. A formal security analysis is given to prove the security of MLR-DVMRS. Besides the comprehensive functionalities of MLR-DVMRS, experimental results demonstrate that the efficiency of MLR-DVMRS is superior to MRSE (a multi-keyword ranked search over encrypted cloud data scheme) for large data collection.

Semantic-aware multi-keyword ranked search scheme over encrypted cloud data

Traditional searchable encryption schemes mostly adopt the TF-IDF (term frequency - inverse document frequency) model which ignores the semantic association between keywords and documents. It is a challenge to design an effective and secure semantic-aware search scheme. The topic model is based on “high-order co-occurrence”, i.e., how often words co-occur in different contexts. Hence, it can be utilized for modeling the latent semantics among texts. In this paper, we propose a novel privacy-persevering searchable encryption scheme based on the Latent Dirichlet Allocation (LDA) topic model. Documents are modeled by LDA, and the concept of topics is utilized to generate a document-topic relevance matrix and query topic vectors. The matrix is used as the index for the proposed scheme. The secure inner product operation is adopted to encrypt the index and query topic vectors, which provides accurate topic relevance score calculation between encrypted index and trapdoors. To improve the efficiency of our basic scheme, we adopt a special complete binary tree and use the “Greedy Depth First Search” algorithm. Our evaluation results demonstrate the effectiveness of our scheme.

Efficient Ranked Multi-Keyword Retrieval With Privacy Protection for Multiple Data Owners in Cloud Computing

With the significant rise in the demand for infinite storage resource and high-quality retrieval service, cloud computing is deemed to be a highly remarkable technology. For privacy concerns, several research works on ranked multi-keyword search over the encrypted cloud data under multiple data owner model have been developed. However, most of these schemes are vulnerable to keyword guessing attacks and equivalence test attack. Besides, they cannot accurately return the top - $k$ ranked search results from the encrypted data of different data owners to the data users. Obviously, these drawbacks may incur leakage of privacy-sensitive keyword information and the inaccuracy of returned search results. Therefore, in this article, we first propose a novel efficient ranked multi-keyword retrieval scheme with keyword privacy for multiple data owners, which empowers the cloud server to perform multi-keyword search over the cloud data and then, return the top - $k$ ranked search results to data users without leaking any keyword and trapdoor information. Additionally, we show through rigorous security analysis that our scheme is secure against the attacks launched by inside attackers and outside attackers. Finally, the performance evaluation indicates that our scheme has more satisfactory features than the existing ranked multi-keyword search schemes.

Security analysis of secure kNN and ranked keyword search over encrypted data

Wong et al. proposed a novel symmetric encryption scheme in which we can find the k-nearest neighbors from encrypted data and an encrypted query. Their scheme uses a pair of encryption functions that has an inner-product preserving property. Because of this property, the pair of encryption functions has been used in several encryption schemes involving ranked multi-keyword search as applications. On the other hand, Yao et al. pointed out that the pair of encryption functions is insecure when the attacker can get plaintext–ciphertext pairs. To prevent this attack, some countermeasures are given in the applications, e.g., randomizing plaintexts before encrypted. In this paper, we reanalyze the security of the inner-product preserving encryption functions. We first discuss the countermeasures against Yao et al.’s attack used in the applications. In particular, we show that one of them is ineffective. Next, we show that the first encryption function is breakable by the known plaintext attack by showing a concrete key-recovery procedure. Unlike Yao et al.’s attack, our attack does not use the second encryption function.

Secure conjunctive multi-keyword ranked search over encrypted cloud data for multiple data owners

Recently, secure search over encrypted cloud data has become a hot research spot and challenging task. A number of secure search schemes have been proposed to try to meet this challenge. However, most of them only consider the single data owner model. In this paper, we propose a conjunctive multi-keyword ranked secure search scheme for multiple data owners. To guarantee data security and system flexibility in the multiple data owners environment, we design an ingenious secure query scheme that allows each data owner to adopt randomly chosen temporary keys to build secure indexes for different data files. An authorized data user does not need to know these temporary keys of constructing indexes and can instead randomly choose another temporary query keys to encrypt query keywords, while the cloud server can correctly perform keywords matching over encrypted data files. To rank the query results of a conjunctive multi-keyword query, the cloud server computes the similarity scores between the query and its query results according to encrypted relevance scores of keywords without obtaining any sensitive information. Extensive experiments demonstrate the correctness and practicality of the proposed scheme.

Multi-keyword ranked searchable encryption scheme with access control for cloud storage

With the advent of cloud computing, data owners are motivated to outsource their data to public clouds for decreasing the cost of management systems. For protecting data privacy, sensitive data must be encrypted before outsourcing. So, equipping cloud server with search service over encrypted data is an important issue. Considering the large number of data users and documents in the cloud, users may be interested to perform multi-keyword search and receive the most related data. In this paper, we investigate the Pasupuleti et al.’s scheme which is a multi-keyword ranked search over encrypted cloud data. Their scheme has problems in index construction, trapdoor generation and search procedures. We address these problems and suggest a multi-keyword ranked search over encrypted data on cloud storage. The proposed ranked searchable encryption scheme enhances system usability by ranking results instead of just sending undifferentiated results and ensures file retrieval accuracy. We also use the relevance score from information retrieval to build a secure searchable index, and apply an additive order-preserving encryption to protect the sensitive scores of files. Our scheme also guarantees access control of users during the data retrieval by attribute-based encryption. Analysis shows that our scheme is secure and efficient for cloud storage.

A Multi-Keyword Ranked Search Method On Encrypted Data Using Symmetric Encryption Over Cloud

Data security and privacy have grown to be key concerns as cloud computing usage increases. It is difficult to do effective keyword-based searches on encrypted data kept in the cloud due to the inherent tension between searchability and confidentiality. This study suggests a brand-new multi-keyword ranked search methodology that permits effective searching on material that has been encrypted with symmetric encryption methods. The suggested approach guarantees data privacy while enabling users to get appropriate search results based on a variety of terms. To protect data privacy and accomplish effective search operations, the method makes use of secure encryption techniques and index creation methods. Experimental findings show that the suggested strategy is successful and efficient.

Enhanced Security for Dynamic Multi Keyword Ranked Search Using Greedy Best First Search

Today, due to the enormous growth of data technology in cloud computing, the data owners are stimulated to outsource their data in data management to reduce cost and for the convenient. Data confidentiality, in general, can be obtained by encrypting the data before it is outsourced. The client stores the encrypted data to the cloud using Searchable encryption schemes and applies keyword search techniques over cipher text domain. But the main problem in outsourcing is the lack of security and privacy for the sensitive data. So, to overcome this, for privacy requirement, the sensitive data can be encrypted before it is outsourced. Various methods were proposed to preserve the privacy and to provide security to the cloud data which are encrypted. Here in this paper, we proposed a tree-based search method over the encrypted datain the cloud that supports dynamic operation and multi-keyword ranked search. Clearly, the commonly used “inverse document frequency (IDF) term frequency (TF)” model and the vector space method are joined in the query generation and index creation to give multi-keyword ranked search. To get high search efficiency, a tree-type index structure, “Greedy Best-first Search” algorithm is proposed based on the tree- index.

Blockchain-Based Verifiable Multi-Keyword Ranked Search on Encrypted Cloud With Fair Payment

In traditional cloud computing system, searchable encryption is deemed as a core technology to realize data confidentiality protection and information retrieval functions. However, the online payment problem and mutual distrust between cloud platforms and users may hinder the wide adoption of cloud service. In this paper, we construct a blockchain based multi-keyword ranked search with fair payment (BMFP) system, which leverages smart contracts to verify the correctness and completeness of the search result, and automatically execute the fair payment operations. The system realizes public verifiability on a multi-keyword ranked search result. The data owner manages the search authority, and a concrete fair payment smart contract is designed. The BMFP is compatible with Ethereum, and the verification algorithm executed by the smart contract is cost-efficient.

Dynamic Multi-Keyword Ranked Search Based on Bloom Filter Over Encrypted Cloud Data

Cloud computing has become a popular approach to manage personal data for the economic savings and management flexibility in recent year. However, the sensitive data must be encrypted before outsourcing to cloud servers for the consideration of privacy, which makes some traditional data utilization functions, such as the plaintext keyword search, impossible. To solve this problem, we present a multi-keyword ranked search scheme over encrypted cloud data supporting dynamic operations efficiently. Our scheme utilizes the vector space model combined with TF $\times $ IDF rule and cosine similarity measure to achieve a multi-keyword ranked search. However, traditional solutions have to suffer high computational costs. In order to achieve the sub-linear search time, our scheme introduces Bloom filter to build a search index tree. What is more, our scheme can support dynamic operation properly and effectively on the account of the property of the Bloom filter, which means that the updating cost of our scheme is lower than other schemes. We present our basic scheme first, which is secure under the known ciphertext model. Then, the enhanced scheme is presented later to guarantee security even under the known background model. The experiments on the real-world data set show that the performances of our proposed schemes are satisfactory.

Fine-Grained Ranked Multi-Keyword Search Over Hierarchical Data for IoT-Oriented Health System

With the rapid advance of the Internet of Things (IoT) and cloud computing technologies, the IoT-oriented health is expected to greatly improve the quality of healthcare service. However, data security and privacy concerns have become one of the biggest issues in smart health applications. As a potential and promising solution, attribute-based keyword search (ABKS) can provide fine-grained keyword search and access control over the encrypted data at the same time. Nevertheless, prior ABKS schemes cannot simultaneously support fine-grained, effective, and accurate data retrieval over hierarchical data. In this paper, to tackle these issues, we propose a fine-grained ranked multi-keyword search scheme over hierarchical data by leveraging ciphertext-policy hierarchical attribute-based encryption (CP-HABE) and ranked multi-keyword search (RMKS) technologies. Then, we prove that our proposed scheme is selectively secure through security analysis and we also show the practicability and feasibility of the proposed scheme by performance evaluation.