Ranked Keyword Search Research Articles

Attribute-Based Expressive and Ranked Keyword Search Over Encrypted Documents in Cloud Computing

Attribute-based keyword search (ABKS) has been proposed to realize fine-grained access control and provide search service in cloud computing. However, most ABKS schemes focus on single or conjunctive keyword search, while the recent Boolean keyword search schemes only support monotonic query formula mainly involving AND, OR and threshold operators. How to support more expressive Boolean query formulas and return the corresponding accurate search results to users have become challenges for practical ABKS over ciphertexts. In this paper, we introduce an attribute-based expressive and ranked keyword search scheme over encrypted documents named ABERKS, which allows authorized users to submit expressive Boolean query formulas involving AND, OR, NOT and threshold operators. ABERKS utilizes a non-monotonic access tree structure to construct the query formula, and further leverages extended Boolean model to rank the search results. Specifically, the users are able to define the weights in the query formula, and get the relevance score of each matched ciphertext if the attributes and keywords are both satisfied. We prove the security of ABERKS against chosen keyword attack under selective ciphertext policy model and against keyword guessing attack, and also conduct extensive experiments to show the efficiency and practicality of ABERKS.

Verifiable Semantic-Aware Ranked Keyword Search in Cloud-Assisted Edge Computing

Ranked keyword search has gained Ranked keyword search has gained traction due to its attractive properties such as flexibility and accessibility. However, most existing ranked keyword search schemes ignore the semantic associations between the documents and queries. To solve this challenging issue in cloud-assisted edge computing, we first design the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> emantic-aware <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</b> anked <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> ulti-keyword <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> earch (SRMS) scheme by adopting the Latent Dirichlet Allocation (LDA) topic model and the Chinese Remainder Theorem (CRT)-based secret sharing mechanism. Considering that the cloud server may be malicious, we implement a basic verification mechanism in SRMS to verify the correctness and completeness of search results and extend this verification mechanism in cloud-assisted edge computing scenarios. Formal security analysis proves that SRMS and extended result verification mechanisms are secure in both the known ciphertext model and the known background model. Extensive experiments using the real-world dataset demonstrate that SRMS is efficient and practical.

Authorized Data Secure Access Scheme With Specified Time and Relevance Ranked Keyword Search for Industrial Cloud Platforms

With the dramatically increase in the scale of Internet data, industrial cloud platforms have put forward higher requirements for data access control and keyword search. Although existing attribute-based encryption technology is suitable for realizing data access control, the problems of large number of matching contents of search results and high computational overhead remain exist. In light of this, an authorized data secure access scheme with specified time and relevance ranked keyword search is proposed to achieve accurate matching of the search results and reduce user’s cost. First, an authorization token on attribute-based access policy and the specified time is constructed. According to the token, the data meeting the access policy is selected within the specified time. Second, we leverage order-preserving index and extended keyword vector to construct keyword index, then the search results of user specified amount are sorted by relevance score. With the use of specified time and relevance for twice screening, the user can get the relevant search results accurately. Finally, the security proof and efficiency analysis show that the scheme is of feasibility.

Ranked Keyword Search over Encrypted Cloud Data Through Machine Learning Method

Ranked keyword search over encrypted data has been extensively studied in cloud computing as it enables data users to find the most relevant results quickly. However, existing ranked multi-keyword search solutions cannot achieve efficient ciphertext search and dynamic updates with forward security simultaneously. To solve the above problems, we first present a basic Machine Learning-based Ranked Keyword Search (ML-RKS) scheme in the static setting by using the k-means clustering algorithm and a balanced binary tree. ML-RKS reduces the search complexity without sacrificing the search accuracy, but is still vulnerable to forward security threats when applied in the dynamic setting. Then, we propose an Enhanced ML-RKS (called ML-RKS <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$^{+}$</tex></formula> ) scheme by introducing a permutation matrix. ML-RKS <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$^{+}$</tex></formula> prevents cloud servers from making search queries over newly added files via previous tokens, thereby achieving forward security. The security analysis proves that our schemes protect the privacy of indexes, query tokens and keywords. Empirical experiments using the real-world dataset demonstrate that our schemes are efficient and feasible in practical applications.

A Survey on Secure Ranked Keyword Search over Outsourced Encrypted Cloud Data

In this paper we present a survey on keyword based searching algorithms. Various searching techniques are used for retrieving the encrypted data from cloud servers. This survey work involves a comparative study of these keyword based searching algorithms. It concludes that till now multi-keyword ranked search MRSE scheme is the best methodology for searching the encrypted data.

Semantic-based Compound Keyword Search over Encrypted Cloud Data

Keyword search over encrypted data is essential for accessing outsourced sensitive data in cloud computing. In some circumstances, the keywords that the user searches on are only semantically related to the data rather than via an exact or fuzzy match. Hence, semantic-based keyword search over encrypted cloud data becomes of paramount importance. However, existing schemes usually depend upon a global dictionary, which not only affects the accuracy of search results but also causes inefficiency in data updating. Additionally, although compound keyword search is common in practice, the existing approaches only process them as single words, which split the original semantics and achieve low accuracy. To address these limitations, we initially propose a compound concept semantic similarity (CCSS) calculation method to measure the semantic similarity between compound concepts. Next, by integrating CCSS with Locality-Sensitive Hashing function and the secure kk<; inline-graphic xlink:href="wang-ieq1-2847318.gif"/>-Nearest Neighbor scheme, a semantic-based compound keyword search (SCKS) scheme is proposed. SCKS achieves not only semantic-based search but also multi-keyword search and ranked keyword search. Additionally, SCKS also eliminates the predefined global library and can efficiently support data update. The experimental results on real-world dataset indicate that SCKS introduces low overhead on computation and the search accuracy outperforms the existing schemes.

Practical Multi-Keyword Ranked Search With Access Control Over Encrypted Cloud Data

With the explosive growth of data volume in the cloud computing environment, data owners are increasingly inclined to store their data on the cloud. Although data outsourcing reduces computation and storage costs for them, it inevitably brings new security and privacy concerns, as the data owners lose direct control of sensitive data. Meanwhile, most of the existing ranked keyword search schemes mainly focus on enriching search efficiency or functionality, but lack of providing efficient access control and formal security analysis simultaneously. To address these limitations, in this article we propose an efficient and privacy-preserving <b>M</b> ulti-keyword <b>R</b> anked <b>S</b> earch scheme with <b>F</b> ine-grained access control (MRSF). MRSF can realize highly accurate ciphertext retrieval by combining coordinate matching with Term Frequency-Inverse Document Frequency (TF-IDF) and improving the secure <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> NN method. Besides, it can effectively refine users’ search privileges by utilizing the polynomial-based access strategy. Formal security analysis shows that MRSF is secure in terms of confidentiality of outsourced data and the privacy of index and tokens. Extensive experiments further show that, compared with existing schemes, MRSF achieves higher search accuracy and more functionalities efficiently.

Efficient top representative for multi-authorship encrypted cloud data to assist cognitive search

Cloud computing is gaining ground in the digital and business world. It delivers storage service for user access using Internet as a medium. Besides the numerous benefits of cloud services, migrating to public cloud storage leads to security and privacy concerns. Encryption method protects data privacy and confidentiality. However, encrypted data stored in cloud storage reduces the flexibility in processing data. Therefore, the development of new technologies to search top representatives from encrypted public storage is the current requirement. This paper presents a similarity-based keyword search for multi-author encrypted documents. The proposed Authorship Attribute-Based Ranked Keyword Search (AARKS) encrypts documents using user attributes, and returns ranked results to authorized users. The scheme assigns weight to index vectors by finding the dominant keywords of the specific authority document collection. Search using the proposed indexing prunes away branches and processes only fewer nodes. Re-weighting documents using the relevant feedback also improves user experience. The proposed scheme ensures the privacy and confidentiality of data supporting the cognitive search for encrypted cloud data. Experiments are performed using the Enron dataset and simulated using a set of queries. The precision obtained for the proposed ranked retrieval is 0.7262. Furthermore, information leakage to a cloud server is prevented, thereby proving its suitability for public storage.

Variable Techniques for Ciphertext Retrieval through Encrypted Data

In recent years, Cloud computing provides strong grip and flexible access on outsource data, cloud storage, data privacy is major concern from to outsource their data, authenticated users are allowed to access this storage to prevent important and sensitive data. For data protection and utilization, we encrypt our sensitive data before outsourced our data because cannot trust storage server, are un-trusty but on other hand, data retrieval in encrypted format from cloud, is challenging task for data utilization, was encrypted from plaintext to ciphertext, when retrieves from cloud storage. However, searchable encryption schemes used Boolean search but they are unable to make data utilization for huge data and failed to handle multi-users access to retrieve ciphertext from cloud and user’s authentication. In this paper, we are using ranked keyword search over encrypted data by going k-documents at storage and using a Hierarchical Clustering Method is designed to guide more search semantics with an additional feature of making the system to cope the demand for fast ciphertext k-search in large scale environments explored the relevance score such as massive and big cloud data. This threshold splits the consequential clusters into sub-clusters until the necessity on the maximum size of cluster is reached. To make fetching search to be secure and privacy-preserving, it is built an index for searching on cloud data and retrieve the most relevant files from cloud. To defending privacy breaches from unauthorized users, users will go through authentication process and data retrieval time as well.

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.

Catch You if You Misbehave: Ranked Keyword Search Results Verification in Cloud Computing

With the advent of cloud computing, more and more people tend to outsource their data to the cloud. As a fundamental data utilization, secure keyword search over encrypted cloud data has attracted the interest of many researchers recently. However, most of existing researches are based on an ideal assumption that the cloud server is “curious but honest”, where the search results are not verified. In this paper, we consider a more challenging model, where the cloud server would probably behave dishonestly. Based on this model, we explore the problem of result verification for the secure ranked keyword search. Different from previous data verification schemes, we propose a novel deterrent-based scheme. With our carefully devised verification data, the cloud server cannot know which data owners, or how many data owners exchange anchor data which will be used for verifying the cloud server's misbehavior. With our systematically designed verification construction, the cloud server cannot know which data owners’ data are embedded in the verification data buffer, or how many data owners’ verification data are actually used for verification. All the cloud server knows is that, once he behaves dishonestly, he would be discovered with a high probability, and punished seriously once discovered. Furthermore, we propose to optimize the value of parameters used in the construction of the secret verification data buffer. Finally, with thorough analysis and extensive experiments, we confirm the efficacy and efficiency of our proposed schemes.

BFSI-B: An improved K-hop graph reachability queries for cyber-physical systems

Cyber-physical systems, encompass the real physical space and virtual cyber space for providing advanced service for humans, which together, are also namely Internet of Things. The complex and large scale relationships among nodes contain the potential information of user, which is required and essential for providing high quality personalized service. Graph is used to represent, make fusion and process the relationships data, which has been used in many domains with traditional small data sets. K-hop reachability query answering in graphs is seeing a growing number of applications, such as ranked keyword search in databases, social networking, ontology reasoning and bioinformatics. Currently, techniques for efficient evaluation of such queries are based on vertex-cover technology. These methods suffer from a lack of scalability; that is, they cannot be applied to very large graphs. To solve these problems, we propose the compound-index method, namely BFSI-B, which uses the special index to decrease the k-hop reachability query time and improve pruning efficiency. The theoretical analysis and experiment results demonstrate that our method has a smaller index size and a faster query time than the k-reach method, in both dense graphs and very large graphs.

Verification of Ranked Keyword Search in Cloud Computing

Verification of Ranked Keyword Search in Cloud Computing

PIMRS: achieving privacy and integrity-preserving multi-owner ranked-keyword search over encrypted cloud data

Because of the flexibility and convenience brought by cloud computing, it has been adopted in many applications. To preserve the privacy of cloud data, data owner often encrypts all sensitive data files, which makes the keyword search application based on plaintext a very challenging task. Therefore, several privacy-preserving keyword search algorithms have been developed recently, and most of these works support only single-data-owner settings. However, there are always more than one data owners in real applications, which are much more complex and challenging than single-owner scenario. To support multi-owner keyword search, those prior search algorithms need to be repeated several times, because each data owner intends to encrypt his own files with a unique private-key separately. It is absolutely not an efficient way. In this paper, we propose a privacy and integrity-preserving multi-owner ranked-keyword search scheme termed PIMRS. In the PIMRS, we exploit an asymmetric scalar-product encryption function based on the TF×IDF rule to preserve data privacy and to obtain more precise search results. Furthermore, a circular bi-direction-linked list based scheme is proposed to preserve the integrity of search results, which also enables the misbehaviors of cloud server to be detected. The security analysis of PIMRS shows its privacy and integrity property, and extensive experiments based on real-world data set confirm the high efficiency of proposed schemes. Copyright © 2016 John Wiley & Sons, Ltd.

An efficient and secure privacy-preserving approach for outsourced data of resource constrained mobile devices in cloud computing

Outsourcing of data into cloud has become an effective trend in modern day computing due to its ability to provide low-cost, pay-as-you-go IT services. Although cloud based services offer many advantages, privacy of the outsourced data is a big concern. To mitigate this concern, it is desirable to outsource sensitive data in an encrypted form but cost of encryption process would increase the heavy computational overhead on thin clients such as resource-constrained mobile devices. Recently, several keyword searchable encryption schemes have been described in the literature. However, these schemes are not effective for resource-constrained mobile devices, because the adopted encryption system should not only support keyword search over the encrypted data but also offer high performance. In this paper, we propose an efficient and secure privacy-preserving approach for outsourced data of resource-constrained mobile devices in the cloud computing. Our approach employs probabilistic public key encryption algorithm for encrypting the data and invoke ranked keyword search over the encrypted data to retrieve the files from the cloud. We aim to achieve an efficient system for data encryption without sacrificing the privacy of data. Further, our ranked keyword search greatly improves the system usability by enabling ranking based on relevance score for search result, sends top most relevant files instead of sending all files back, and ensures the file retrieval accuracy. As a result, data privacy ensures and computation, communication overheads in reduction. Thorough security and performance analysis, we prove that our approach is semantically secure and efficient.

Searching on Encrypted Cloud Documentsusing Keywords

Sensitive cloud data have to be encrypted to protect information safety measures, before outsourcing. The encryption technique makes effective data utilization service a very challenging job. Traditional searchable encryption technique allows users to securely search over encrypted data through keywords. The safety allows information searching scheme provides solution for secure ranked keyword search over encrypted obscure information. Ordered search greatly developed technique usability by enabling search result relevance ranking instead of sending undifferentiated results and further ensures the accuracy of folder recovery. The numerical determine process, i.e., importance gain, from information retrieval is explored to build a secure searchable index. One-to-many orderpreserving mapping technique is developed to properly protect those sensitive score information. The system facilitates server-side ranking without losing keyword security. The method is improved to importance gain active development. Investigate outcome verification is also provided in the system. One-to-many order-preserving mapping method is also enhanced in reversible manner. The similarity analysis technique is used to identify the query results under the cloud data storage.

Enabling Secure and Efficient Ranked Keyword Search over Outsourced Cloud Data

Cloud computing economically enables the paradigm of data service outsourcing. However, to protect data privacy, sensitive cloud data have to be encrypted before outsourced to the commercial public cloud, which makes effective data utilization service a very challenging task. Although traditional searchable encryption techniques allow users to securely search over encrypted data through keywords, they support only Boolean search and are not yet sufficient to meet the effective data utilization need that is inherently demanded by large number of users and huge amount of data files in cloud. In this paper, we define and solve the problem of secure ranked keyword search over encrypted cloud data. Ranked search greatly enhances system usability by enabling search result relevance ranking instead of sending undifferentiated results, and further ensures the file retrieval accuracy. Specifically, we explore the statistical measure approach, i.e., relevance score, from information retrieval to build a secure searchable index, and develop a one-to-many order-preserving mapping technique to properly protect those sensitive score information. The resulting design is able to facilitate efficient server-side ranking without losing keyword privacy. Thorough analysis shows that our proposed solution enjoys “as-strong-as-possible” security guarantee compared to previous searchable encryption schemes, while correctly realizing the goal of ranked keyword search. Extensive experimental results demonstrate the efficiency of the proposed solution.

Syslog 데이터의 의미론적 검색을 위한 XML 기반의 모델링

Event logging plays increasingly an important role in system and network management, and syslog is a de-facto standard for logging system events. However, due to the semi-structured features of Common Log Format data most studies on log analysis focus on the frequent patterns. The extensible Markup Language can provide a nice representation scheme for structure and search of formatted data found in syslog messages. However, previous XML-formatted schemes and applications for system logging are not suitable for semantic approach such as ranking based search or similarity measurement for log data. In this paper, based on ranked keyword search techniques over XML document, we propose an XML tree structure through a new data modeling approach for syslog data. Finally, we show suitability of proposed structure for semantic retrieval.