Supporting Keyword Search Research Articles

Solo: Data Discovery Using Natural Language Questions Via A Self-Supervised Approach

Most deployed data discovery systems, such as Google Datasets, and open data portals only support keyword search. Keyword search is geared towards general audiences but limits the types of queries the systems can answer. We propose a new system that lets users write natural language questions directly. A major barrier to using this learned data discovery system is it needs expensive-to-collect training data, thus limiting its utility. In this paper, we introduce a self-supervised approach to assemble training datasets and train learned discovery systems without human intervention. It requires addressing several challenges, including the design of self-supervised strategies for data discovery, table representation strategies to feed to the models, and relevance models that work well with the synthetically generated questions. We combine all the above contributions into a system, Solo, that solves the problem end to end. The evaluation results demonstrate the new techniques outperform state-of-the-art approaches on well-known benchmarks. All in all, the technique is a stepping stone towards building learned discovery systems.

A multi-keyword searchable encryption sensitive data trusted sharing scheme in multi-user scenario

Searchable encryption technology provides a solution for the trusted sharing of sensitive data. A large amount of encrypted data stored in data sharing servers can support keyword search without exposing data privacy, which has attracted more and more scholars’ attentions and researches. However, some multi-keyword searchable encryption schemes that deal with multi-semantic data still have security problems or low efficiency. To improve the security and efficiency of trusted sharing of sensitive data, we propose a multi-keyword searchable encryption scheme. The scheme designs a sensitive data sharing architecture based on blockchain technology, and the tamper-proof of distributed ledgers ensures the authenticity of encrypted data and indexes, as well as the supervision of sharing behaviors. On this basis, we improved the inverted index structure to achieve efficient multi-keyword searchable encryption while avoiding keyword-pair result pattern leakage. Our proposed scheme has passed formal security analysis, and simulation results show that the multi-keyword searchable encryption scheme is efficient.

Efficient Strong Privacy-Preserving Conjunctive Keyword Search Over Encrypted Cloud Data

Searchable symmetric encryption (SSE) supports keyword search over outsourced symmetrically encrypted data. Dynamic searchable symmetric encryption (DSSE), a variant of SSE, further enables data updating. Most DSSE works with conjunctive keyword search primarily consider forward and backward privacy. Ideally, the server should only learn the result sets involving all keywords in the conjunction. However, existing schemes suffer from keyword pair result pattern (KPRP) leakage, revealing the partial result sets containing two of query keywords. We propose the first DSSE scheme to address aforementioned concerns that achieves strong privacy-preserving conjunctive keyword search. Specifically, our scheme can maintain forward and backward privacy and eliminate KPRP leakage, offering a higher level of security. The search complexity scales with the number of documents stored in the database in several existing schemes. However, the complexity of our scheme scales with the update frequency of the least frequent keyword in the conjunction, which is much smaller than the size of the entire database. Besides, we devise a least frequent keyword acquisition protocol to reduce frequent interactions between clients. Finally, we analyze the security of our scheme and evaluate its performance theoretically and experimentally. The results show that our scheme has strong privacy preservation and efficiency.

Verifiable Keyword Search Encryption Scheme That Supports Revocation of Attributes

In recent years, searchable encryption technology and attribute encryption technology have been widely used in cloud storage environments, and attribute-based searchable encryption schemes can both achieve the retrieval of encrypted data and effectively solve the access control problem. Considering that existing attribute-based searchable encryption schemes for cloud storage only support keyword search and do not support attribute revocation, most of the schemes that support attribute revocation only consider the computational overhead of users and ignore the large amount of computational resources consumed by attribute authorization centers when updating keys. In addition, keyword search may lead to partial errors in the returned search results, leading to the wastage of computational and broadband resources. To solve these issues, this paper proposes an attribute-based searchable encryption scheme that supports attribute revocation and is verifiable. To realize fine-grained ciphertext search of encrypted data, support scenarios of dynamic changes of user attributes, and ensure that third-party servers perform the search process reliably and honestly while minimizing computation and storage costs, first, this paper implements attribute revocation with the attribute authorization center by creating a user revocation list and an attribute key revocation list. At the same time, the system updates the attribute key at the time of user search request, which effectively reduces the computational overhead. Second, a third-party auditor is introduced to ensure the correctness of the search results. Finally, the security of this paper is verified by theoretical analysis, and the efficiency and practicality of this paper are verified by comparing it to other schemes through simulation experiments.

Reliable Data Sharing by Certificateless Encryption Supporting Keyword Search Against Vulnerable KGC in Industrial Internet of Things

Outsourcing Industrial Internet of Things (IIoT) data on the cloud extends the diversity of data analysis for decision making with minimized costs in communications and storage. However, it is adverse to the confidentiality of IIoT data from the owner as access control is performed by honest-but-curious platforms. Although the encryption strategy guarantees data security, it hinders deliverance due to its inbred all-or-nothing decryption. Certificateless encryption supporting keyword search eliminates the overhead of certificates and privileges to retrieve the required data through encrypted keyword search. However, most of the prior works are precarious against a malicious key generation center, which exposes data protected by users’ private keys. We design a certificateless secure data sharing by uniting the functionalities of encryption and access control on search. Our technique resists in/outside keyword guessing attacks and sustains on-demand user revocation. Besides, it achieves Girault’s Level-3 security in the standard model. Nonetheless, empirical performance analysis under a suitable scenario exhibits its feasibility compared to the other related schemes.

Encryption Switching Service: Securely Switch Your Encrypted Data to Another Format

Big data analytics has been regarded as a promising technology to yield better insights into future development by government and industry. Data collection and aggregation are necessary pre-steps to enable data analysis. However, data may be dispersed across multiple places and in different formats. Even worse, data can be encrypted under various encryption mechanisms when data owners try to secure the confidentiality of the data. This makes data aggregation extremely challenging, if not impossible, especially when the encryption keys cannot be shared for various reasons. In this paper, we take the first step in addressing this problem. More specifically, we propose a new notion of cross-domain encryption switching service that securely bridges two well-studied encryption mechanisms, namely traditional public key encryption and identity-based encryption. As of independent interest, our notion supports keyword search over encrypted data, i.e., after encryption switching one may search over the (outsourced) data without loss of data and query secrecy. We provide a provably-secure instantiation satisfying the notion, and further present the efficiency analysis to show the scalability. Our proposed scheme may be applicable in multi-domain cloud storage system.

Multi-Value-Independent Ciphertext-Policy Attribute Based Encryption with Fast Keyword Search

ABKS has drawn much attention from research and industry in recent years, an ABKS scheme is an encryption scheme that supports keyword search and access control. Attribute-Based Encryption is a public key encryption that enables users to encrypt and decrypt message based on attributes. In a typical implementation, the size of the ciphertext is proportional to the number of attributes associated with it and the decryption time is proportional to the number of attributes used during decryption. Inherit from ABE technology, the computation cost and ciphertext size in most ABKS schemes grow with the complexity of the access policy. On the other hand, we found that the traditional ABKS schemes cannot resist our secret-key-recovery attack. To deal with the above problems, we present new ciphertext policy attribute based encryption with fast keyword search constructions. Our constructions preserve the fine-grained access control inherited from the ABE system while supporting hidden policy and fast keyword search. Our constructions feature multi-value-independent compared with the existing attribute based searchable encryption schemes. The performance analysis demonstrates the efficiency of our constructions. We offer rigorous security proof of our second scheme, which is IND-CKA and IND-CPA secure.

A Secure Cloud Data Sharing Protocol for Enterprise Supporting Hierarchical Keyword Search

Cloud storage becomes the priority for storing and sharing data for enterprise users. Encrypting prior to uploading data to the cloud is the best way to protect business secrets, however, it hinders the convenient operations on plaintexts, such as searching over the cloud data. In addition, employees in an enterprise have multiple layer structures and a higher layer employee should have the privilege to monitor the lower layer employees’ data to check if these users violate the regulation without letting the employees be aware of. Public key encryption with keyword search (PEKS) is a well-known cryptographic primitive suitable for secure cloud storage, which supports keyword search without decryption in public key encryption settings. Unfortunately, no existing PEKS scheme supports the monitoring function without authorization from the sender. To address this issue, we propose a variant of PEKS named Hierarchical Public Key Encryption with Keyword Search (HPEKS) and provide a semi-generic construction utilizing a public key tree (PKTree) and a PEKS scheme. To better suit for the enterprise secret data sharing, we build an advanced HPEKS scheme, named designated-tester decryptable hierarchical public key encryption with keyword search (dDHPEKS), which enjoys stronger security and integrates the public key and symmetric key encryptions. We prove our dDHPEKS scheme secure under the security definition in the random oracle model. Particularly, it satisfies the security against outside offline keyword guessing attacks and furthermore, enjoys the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">transparency</i> property so that the sender does not need to know the internal hierarchy structure of an enterprise in order to share encrypted data to the enterprise. Theoretical evaluation and concrete experiments show that our dDHPEKS scheme has comparable running efficiency with existing PEKS schemes.

Efficient Boolean Keywords Search over Encrypted Cloud Data in Public Key Setting

Searchable public key encryption- (SPE-) supporting keyword search plays an important role in cloud computing for data confidentiality. The current SPE scheme mainly supports conjunctive or disjunctive keywords search which belongs to very basic query operations. In this paper, we propose an efficient and secure SPE scheme that supports Boolean keywords search, which is more advanced than the conjunctive and disjunctive keywords search. We first develop a keyword conversion method, which can change the index and Boolean keywords query into a group of vectors. Then, through applying a technique so-called dual pairing vector space to encrypt the obtained vectors, we propose a concrete scheme proven to be secure under chosen keyword attack. Finally, we put forward a detailed theoretical and experimental analysis to demonstrate the efficiency of our scheme.

An effective quality analysis of XML web data using hybrid clustering and classification approach

An effective quality analysis of XML web data using clustering and classification approach is used in our proposed method. XML is turning into a standard in representation of data, it is attractive to support keyword search in XML database. A keyword search searches for words anyplace in record. It is developed as best worldview for finding data on web. The most imperative prerequisite for the keyword search is to rank the consequences of question so that the most pertinent outcomes show up. Here, we gather more XML documents. Followed by that, feature extraction occurs. Since the selected feature contains both relevant as well as irrelevant features it is essential to filter the irrelevant features. For the purpose of selecting, the relevant features probability-based feature selection method is used. Then for clustering the relevant features on the basis of keywords weighted fuzzy c means clustering algorithm is used. In order to assess the XML data quality, optimal neural network (ONN) classifier is utilized. In this ONN classifier in order to select the optimal weights, whale optimization algorithm is used. Thus, the web pages are effectively ranked. The efficiency of the proposed method is assessed using clustering and classification accuracy, RMSE, and search time. The proposed method is implemented in JAVA.

Toward Forward Secure SSE Supporting Conjunctive Keyword Search

The primitive of Searchable Symmetric Encryption (SSE) allows a server to perform search directly over encrypted documents. Recently, forward secure SSE (FSSE) has attracted significant attention due to its secure property for document update. That is, it can prevent the server from learning whether the newly updated document contains previously queried keywords. Most of the existing FSSE schemes are designed for single keyword search. For the case of conjunctive keyword search, prior dedicated FSSE constructions either require inefficient tools or suffer from significant leakage (i.e., it leaks the documents matching each queried keywords). To deal with this challenge, we propose a general framework to design efficient conjunctive keyword FSSE schemes without significant leakage: a basic scheme FOXT-B and an enhanced one FOXT-E. Specifically, in our basic scheme, we combine XSet structure in the Oblivious Cross-Tags protocol (CRYPTO 2013) with a single keyword FSSE to support conjunctive keyword search. However, this simple combination is limited to support document update. That is, the user cannot perform search until all the keyword-document pairs in a document have been updated. To support flexible keyword-document update, we design a new XSet for our enhanced scheme, in which the user can perform search at any time. Finally, we present a formal security analysis and implement our schemes on a real-world database, which demonstrates that our proposed schemes can achieve the desired security goals with practical efficiency.

Enhancing Keyword Query Results Over Database for Improving User Satisfaction

Storing data in relational databases is widely increasing to support keyword queries but search results does not gives effective answers to keyword query and hence it is inflexible from user perspective. It would be helpful to recognize such type of queries which gives results with low ranking. Here we estimate prediction of query performance to find out effectiveness of a search performed in response to query and features of such hard queries is studied by taking into account contents of the database and result list. One relevant problem of database is the presence of missing data and it can be handled by imputation. Here an inTeractive Retrieving-Inferring data imputation method (TRIP) is used which achieves retrieving and inferring alternately to fill the missing attribute values in the database. So by considering both the prediction of hard queries and imputation over the database, we can get better keyword search results.

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.

Supporting Keyword Search for Image Retrieval with Integration of Probabilistic Annotation

The ever-increasing quantities of digital photo resources are annotated with enriching vocabularies to form semantic annotations. Photo-sharing social networks have boosted the need for efficient and intuitive querying to respond to user requirements in large-scale image collections. In order to help users formulate efficient and effective image retrieval, we present a novel integration of a probabilistic model based on keyword query architecture that models the probability distribution of image annotations: allowing users to obtain satisfactory results from image retrieval via the integration of multiple annotations. We focus on the annotation integration step in order to specify the meaning of each image annotation, thus leading to the most representative annotations of the intent of a keyword search. For this demonstration, we show how a probabilistic model has been integrated to semantic annotations to allow users to intuitively define explicit and precise keyword queries in order to retrieve satisfactory image results distributed in heterogeneous large data sources. Our experiments on SBU (collected by Stony Brook University) database show that (i) our integrated annotation contains higher quality representatives and semantic matches; and (ii) the results indicating annotation integration can indeed improve image search result quality.

Secure deduplication storage systems supporting keyword search

Data deduplication is an attractive technology to reduce storage space for increasing vast amount of duplicated and redundant data. In a cloud storage system with data deduplication, duplicate copies of data will be eliminated and only one copy will be kept in the storage. To protect the confidentiality of sensitive data while supporting deduplication, the convergent encryption technique has been proposed to encrypt the data before outsourcing. However, the issue of keyword search over encrypted data in deduplication storage system has to be addressed for efficient data utilization. This paper firstly proposes two constructions which support secure keyword search in this scenario. In these constructions, the integrity of the data can be realized by just checking the convergent key, without other traditional integrity auditing mechanisms. Then, two extensions are presented to support fuzzy keyword search and block-level deduplication. Finally, security analysis is given.

Attribute-based proxy re-encryption with keyword search.

Keyword search on encrypted data allows one to issue the search token and conduct search operations on encrypted data while still preserving keyword privacy. In the present paper, we consider the keyword search problem further and introduce a novel notion called attribute-based proxy re-encryption with keyword search (), which introduces a promising feature: In addition to supporting keyword search on encrypted data, it enables data owners to delegate the keyword search capability to some other data users complying with the specific access control policy. To be specific, allows (i) the data owner to outsource his encrypted data to the cloud and then ask the cloud to conduct keyword search on outsourced encrypted data with the given search token, and (ii) the data owner to delegate other data users keyword search capability in the fine-grained access control manner through allowing the cloud to re-encrypted stored encrypted data with a re-encrypted data (embedding with some form of access control policy). We formalize the syntax and security definitions for , and propose two concrete constructions for : key-policy and ciphertext-policy . In the nutshell, our constructions can be treated as the integration of technologies in the fields of attribute-based cryptography and proxy re-encryption cryptography.

Supporting keyword search in product database

The ability to let users search for products conveniently in product database is critical to the success of e-commerce. Although structured query languages (e.g. SQL) can be used to effectively access the product database, it is very difficult for end users to learn and use. In this paper, we study how to optimize search over structured product entities (represented by specifications) with keyword queries such as "cheap gaming laptop". One major difficulty in this problem is the vocabulary gap between the specifications of products in the database and the keywords people use in search queries. To solve the problem, we propose a novel probabilistic entity retrieval model based on query generation, where the entities would be ranked for a given keyword query based on the likelihood that a user who likes an entity would pose the query. Different ways to estimate the model parameters would lead to different variants of ranking functions. We start with simple estimates based on the specifications of entities, and then leverage user reviews and product search logs to improve the estimation. Multiple estimation algorithms are developed based on Maximum Likelihood and Maximum a Posteriori estimators. We evaluate the proposed product entity retrieval models on two newly created product search test collections. The results show that the proposed model significantly outperforms the existing retrieval models, benefiting from the modeling of attribute-level relevance. Despite the focus on product retrieval, the proposed modeling method is general and opens up many new opportunities in analyzing structured entity data with unstructured text data. We show the proposed probabilistic model can be easily adapted for many interesting applications including facet generation and review annotation.

Keyword-Based Deep Web Database Selection

This paper proposes a keyword-based Deep Web search method: Given keyword queries provided by users,the proposed method on-the-fly selects the databases capturing the queryintent and providing high-quality data.The method,which is much more efficient than Deep Webcrawling,can support keyword search over multiple-domain Deep Web databases,and thus can besmoothly integrated with the existing search engine architecture.In this paper,we focus on key-word-based Deep Web database selection,and studythe research challenges that naturally arisein the proposed method.(1) We introduce an effective model to measure the relevance of database-domain attributes with respect to keyword queries,and propose a random-walk algorithm to compute the relevance fromdatabase query logs.(2) We develop a novel database sampling method for measuring the relevance of databases with respect to queries,in order to select relevant data-bases in the selected domains.We have implemented our methods on real data sets fromthe Chinese Deep Web.The experi mental results show that our methods achieve high effectiveness.

Shared and searchable encrypted data for untrusted servers

Current security mechanisms are not suitable for organisations that outsource their data management to untrusted servers. Encrypting and decrypting sensitive data at the client side is the normal approach in this situation but has high communication and computation overheads if only a subset of the data is required, for example, selecting records in a database table based on a keyword search. New cryptographic schemes have been proposed that support encrypted queries over encrypted data. But they all depend on a single set of secret keys, which implies single user access or sharing keys among multiple users, with key revocation requiring costly data re-encryption. In this paper, we propose an encryption scheme where each authorised user in the system has his own keys to encrypt and decrypt data. The scheme supports keyword search which enables the server to return only the encrypted data that satisfies an encrypted query without decrypting it. We provide a concrete construction of the scheme and give formal proofs of its security. We also report on the results of our implementation.

XKFitler

Most existing XML stream processing systems adopt full structured query languages, such as XPath or XQuery, but they are difficult for ordinary users to learn and use. Keyword search is a user-friendly information discovery technique that has been extensively studied for text documents. This paper presents an XML stream filter system called XKFitler, which is the first system for supporting keyword search over XML stream. In XKFitler, the concepts of XLCA (eXclusive Lowest Common Ancestor) and XLCA Connecting Tree (XLCACT) are used to define the search semantic and results of keywords, and present an approach to filter XML stream according to keywords. The prototype XKFilter is implemented in the experiments.