Multi-keyword Query Research Articles

Privacy preserving search over multiple servers

Search over encrypted data is a hot topic. In this paper, we propose a secure scheme for searching the encrypted servers. Such scheme enables the authorised user to search multiple servers with multi-keyword queries and retrieve the most relevant documents in a descending order with respect to their relevance to the query, without compromising the privacy of his query and the contents of documents he retrieved. In such a setting, each data owner builds his own searchable index, and associates with each document in that index its weight score, which facilitates document ranking. To protect the privacy of these weights, we utilise the privacy preserving mapping (PPM) primitive, while preserving their capability to perform the ranking process. We have conducted several empirical analyses on a real dataset to demonstrate the performance of our proposed scheme.

Privacy preserving search over multiple servers

Search over encrypted data is a hot topic. In this paper, we propose a secure scheme for searching the encrypted servers. Such scheme enables the authorised user to search multiple servers with multi-keyword queries and retrieve the most relevant documents in a descending order with respect to their relevance to the query, without compromising the privacy of his query and the contents of documents he retrieved. In such a setting, each data owner builds his own searchable index, and associates with each document in that index its weight score, which facilitates document ranking. To protect the privacy of these weights, we utilise the privacy preserving mapping (PPM) primitive, while preserving their capability to perform the ranking process. We have conducted several empirical analyses on a real dataset to demonstrate the performance of our proposed scheme.

Substring Position Search over Encrypted Cloud Data Supporting Efficient Multi-User Setup

Existing Searchable Encryption (SE) solutions are able to handle simple Boolean search queries, such as single or multi-keyword queries, but cannot handle substring search queries over encrypted data that also involve identifying the position of the substring within the document. These types of queries are relevant in areas such as searching DNA data. In this paper, we propose a tree-based Substring Position Searchable Symmetric Encryption (SSP-SSE) to overcome the existing gap. Our solution efficiently finds occurrences of a given substring over encrypted cloud data. Specifically, our construction uses the position heap tree data structure and achieves asymptotic efficiency comparable to that of an unencrypted position heap tree. Our encryption takes O ( k n ) time, and the resulting ciphertext is of size O ( k n ) , where k is a security parameter and n is the size of stored data. The search takes O ( m 2 + o c c ) time and three rounds of communication, where m is the length of the queried substring and o c c is the number of occurrences of the substring in the document collection. We prove that the proposed scheme is secure against chosen-query attacks that involve an adaptive adversary. Finally, we extend SSP-SSE to the multi-user setting where an arbitrary group of cloud users can submit substring queries to search the encrypted data.

IJARCCE - Computer and Communication Engineering

As a user types in a keyword query character by character, the type ahead search or search as you type system computes answers on-the-fly. In this paper we are going to study the way of supporting type ahead search on data residing in databases. We are going to focus on the way to support this type of search using the language, MySQL In this paper we are going to study about the way to use the auxiliary indexes stored on tables so that the search performance can be increased, since we need to achieve an interactive speed which can be done by meeting the high performance requirements of the functionalities. In this paper we present solutions to both single keyword and multikeyword queries. We also allow mismatching between query keyword and answers by developing the novel techniques for fuzzy search by using MySQL. Supporting fuzzy search is important when users have limited knowledge about the exact representation of the entities they are looking for, such as people records in an online directory. Therefor it becomes easy for them to retrieve information with the help of this feature. We provide recent search, history, auto-completion also in this paper.

Cost-Effective Replication Schemes for Query Load Balancing in DHT-Based Peer-to-Peer File Searches

In past few years, distributed hash table (DHT)-based P2P systems have been proven to be a promising way to manage decentralized index information and provide efficient lookup services. However, the skewness oIXVHUV∂� SUHIHUHQFHV� regarding keywords contained in a multi-keyword query causes a query load imbalance that combines both routing and response load. This imbalance means long file retrieval latency that negatively influences the overall system performance. Although index replication has a great potential for alleviating this problem, existing schemes did not explicitly address it or incurred high cost. To overcome this issue, we propose, in this paper, an integrated solution that consists of three replication schemes to alleviate query load imbalance while minimizing the cost. The first scheme is an active index replication that is used in order to decrease routing load in the system and to distribute response load of an index among peers that store replicas of the index. The second scheme is a proactive pointer replication that places location information of each index to a predetermined number of peers for reducing maintenance cost between the index and its replicas. The third scheme is a passive index replication that guarantees the maximum query load of peers. The result of simulations indicates that the proposed schemes can help alleviate the query load imbalance of peers. Moreover, it was found by comparison that our schemes are more cost-effective on placing replicas than PCache and EAD.

EMRQ: An Efficient Multi-keyword Range Query Scheme in Smart Grid Auction Market

With the increasing electricity consumption and the wide application of renewable energy sources, energy auction attracts a lot of attention due to its economic benefits. Many schemes have been proposed to support energy auction in smart grid. However, few of them can achieve range query, ranked search and personalized search. In this paper, we propose an efficient multi-keyword range query (EMRQ) scheme, which can support range query, ranked search and personalized search simultaneously. Based on the homomorphic Paillier cryptosystem, we use two super-increasing sequences to aggregate multidimensional keywords. The first one is used to aggregate one buyer’s or seller’s multidimensional keywords to an aggregated number. The second one is used to create a summary number by aggregating the aggregated numbers of all sellers. As a result, the comparison between the keywords of all sellers and those of one buyer can be achieved with only one calculation. Security analysis demonstrates that EMRQ can achieve confidentiality of keywords, authentication, data integrity and query privacy. Extensive experiments show that EMRQ is more efficient compared with the scheme in [3] in terms of computation and communication overhead.

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 m...

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.

Supporting Search-As-You-Type Using SQL in Databases

A search-as-you-type system computes answers on-the-fly as a user types in a keyword query character by character. We study how to support search-as-you-type on data residing in a relational DBMS. We focus on how to support this type of search using the native database language, SQL. A main challenge is how to leverage existing database functionalities to meet the high-performance requirement to achieve an interactive speed. We study how to use auxiliary indexes stored as tables to increase search performance. We present solutions for both single-keyword queries and multikeyword queries, and develop novel techniques for fuzzy search using SQL by allowing mismatches between query keywords and answers. We present techniques to answer first-N queries and discuss how to support updates efficiently. Experiments on large, real data sets show that our techniques enable DBMS systems on a commodity computer to support search-as-you-type on tables with millions of records.

Peano Space Filling Curve Applied in Managing P2P Service Resources

According to the deficiency of Chord algorithm supporting single keyword query only, a P2P framework-Peano Chord (PC) by combining the Peano and Chord is proposed for managing service resources, which combines Peano space filling curve and P2P Service Resources and supports DHT-based multi-keyword query and approximate query by means of Peano index to improve resources searching ability.

An Efficient Privacy-Preserving Rank Query over Encrypted Data in Cloud Computing

In cloud computing,for protecting the privacy of the sensitive cloud data,an effective methodology is to encrypt the data before outsourcing.However,data encryption makes data utilization,e.g.querying,a very challenging task.Though many solutions have been proposed,they are insufficient or even ineffective to achieve efficient multi-keyword rank query and flexible selective query authorization with multiple data owners while keeping strong privacy preserving.In this paper,we propose an efficient privacy-preserving rank query over encrypted data(RQED).Through the improved searchable public key encryption(PKES) with certificateless authentication,we establish the RQED framework.Based on the RQED framework,we design a more sound and privacy-preserving RQED rank function,and propose a hierarchical index based on dynamic Bloom filters.The theoretical analysis and experimental evaluation show that the proposed solutions indeed achieve powerful privacy guarantee,efficient query performance,low communication overhead and effective query authorization control.

Trust‐aware query routing in P2P social networks

SUMMARYQuery efficiency is a significant challenge in peer‐to‐peer (P2P) social networks. In this paper, we build a trust‐aware semantic‐based query routing strategy to improve the searching efficiency and enhance the security. Based on the vector space model, similar semantic ordinary peers set up an unstructured semantic social community. Super peers in each community constitute a structured P2P network and store the keywords information in a global inverted file. We exploit Bloom filters to complete multikeyword query. Furthermore, malicious behaviors may take place in P2P social networks. We then build a community‐based trust mechanism. The intracommunity reputations of ordinary peers in a semantic social community are calculated, and the intercommunity reputations of super peers in the structured P2P network are evaluated. This mechanism combines the local trust and the global trust. The results show that the proposed query scheme is able to increase query efficiency and also decrease query cost with significantly reduced malicious behaviors. Copyright © 2011 John Wiley & Sons, Ltd.

Chord-Based Indexing Model to Support Complex Query and Load Balancing

This paper presents a new model (SMF-Chord) which is expanded from Chord. SMF-Chord uses double-fingerprint, double-mapping and dynamic forwarding mechanism to support multi-attribute multi-keyword fuzzy-matching query, and also has a load balancing mechanism which includes three parts: similar-close transposition, forward balancing and hot-set cache. The experiment results show SMF-Chord has high recall rate with low storage redundancy, and it can effectively balance load when node mapping load, file storage load, or query load is unbalanced.

Efficient fuzzy full-text type-ahead search

Traditional information systems return answers after a user submits a complete query. Users often feel left in the dark when they have limited knowledge about the underlying data and have to use a try-and-see approach for finding information. A recent trend of supporting autocomplete in these systems is a first step toward solving this problem. In this paper, we study a new information-access paradigm, called in which the system searches the underlying data the fly as the user types in query keywords. It extends autocomplete interfaces by allowing keywords to appear at different places in the underlying data. This framework allows users to explore data as they type, even in the presence of minor errors. We study research challenges in this framework for large amounts of data. Since each keystroke of the user could invoke a query on the backend, we need efficient algorithms to process each query within milliseconds. We develop various incremental-search algorithms for both single-keyword queries and multi-keyword queries, using previously computed and cached results in order to achieve a high interactive speed. We develop novel techniques to support fuzzy search by allowing mismatches between query keywords and answers. We have deployed several real prototypes using these techniques. One of them has been deployed to support type-ahead search on the UC Irvine people directory, which has been used regularly and well received by users due to its friendly interface and high efficiency.

다중 키워드 검색에 적합한 동등조인 연산 결과의 동적 관리 기법

인터넷이나 기업체 안에서 생성되는 문서의 수가 빠르게 증가하고 있고 이에 따라 효율적인 문서 검색 서비스의 중요성도 함께 커지고 있다. 이런 검색 환경에서 사용자의 검색 질의를 미리 예측할 수 없기 때문에 문서 내의 키워드를 자동 추출하여 색인어로 사용하는 전문검색(full-text search)이 일반적으로 적용된다. 전문검색을 위해 생성된 색인 파일의 크기는 문서 수 증가로 대용량화 되고, 이런 대용량 색인에 대한 다중 키워드 질의 처리에는 과도한 디스크 비용이 초래될 수 있다. 논문에서는 이런 비용 문제를 해결하기 위해 대용량 문서의 전문검색 시스템에서 다중 키워드 질의를 효율적으로 처리할 수 있게 하는 색인 파일 구조 및 관리 기법을 제안한다. 제안된 방법은 다중 키워드 검색에 적합한 것으로 알려진 역파일을 기본 색인 구조로 하며, 질의 처리의 조인 연산과 랭킹 연산에 적합하도록 색인 파일을 계층화한다. 이를 바탕으로 다중 키워드 질의를 구성할 확률이 높은 키워드 쌍에 대한 조인 연산 결과를 주기억장치 공간에 동적으로 저장함으로써 디스크 사용량을 크게 줄일 수 있다. 논문에서는 제안된 기법의 우수성을 보이기 위해 디스크 비용 모델에 기반한 성능 비교도 수행한다. With an increasing number of documents in the Internet or enterprises, it becomes crucial to efficiently support users' queries on those documents. In that situation, the full-text search technique is accepted in general, because it can answer uncontrolled ad-hoc queries by automatically indexing all the keywords found in the documents. The size of index files made for full-text searches grows with the increasing number of indexed documents, and thus the disk cost may be too large to process multi-keyword queries against those enlarged index files. To solve the problem, we propose both of the index file structure and its management scheme suitable to the processing of multi-keyword queries against a large volume of index files. For this, we adopt the structure of inverted-files, which are widely used in the multi-keyword searches, as a basic index structure and modify it to a hierarchical structure for join operations and ranking operations performed during the query processing. In order to save disk costs based on that index structure, we dynamically store in the main memory the results of join operations between two keywords, if they are highly expected to be entered in users' queries. We also do performance comparisons using a cost model of the disk to show the performance advantage of the proposed scheme.

AlvisP2P

In this paper we present the AlvisP2P IR engine, which enables efficient retrieval with multi-keyword queries from a global document collection available in a P2P network. In such a network, each peer publishes its local index and invests a part of its local computing resources (storage, CPU, bandwidth) to maintain a fraction of a global P2P index. This investment is rewarded by the network-wide accessibility of the local documents via the global search facility. The AlvisP2P engine uses an optimized overlay network and relies on novel indexing/retrieval mechanisms that ensure low bandwidth consumption, thus enabling unlimited network growth. Our demonstration shows how an easy-to-install AlvisP2P client can be used to join an existing P2P network, index local (text or even multimedia) documents with collection-specific indexing mechanisms, and control access rights to them.

Trustworthy keyword search for compliance storage

Intense regulatory focus on secure retention of electronic records has led to a need to ensure that records are trustworthy, i.e., able to provide irrefutable proof and accurate details of past events. In this paper, we analyze the requirements for a trustworthy index to support keyword-based search queries. We argue that trustworthy index entries must be durable--the index must be updated when new documents arrive, and not periodically deleted and rebuilt. To this end, we propose a scheme for efficiently updating an inverted index, based on judicious merging of the posting lists of terms. Through extensive simulations and experiments with two real world data sets and workloads, we demonstrate that the scheme achieves online update speed while maintaining good query performance. We also present and evaluate jump indexes, a novel trustworthy and efficient index for join operations on posting lists for multi-keyword queries. Jump indexes support insert, lookup and range queries in time logarithmic in the number of indexed documents.

An efficient multi-keyword query processing strategy on P2P based Web search

The paper presents a novel benefit based query processing strategy for efficient query routing. Based on DHT as the overlay network, it first applies Nash equilibrium to construct the optimal peer group based on the correlations of keywords and coverage and overlap of the peers to decrease the time cost, and then presents a two-layered architecture for query processing that utilizes Bloom filter as compact representation to reduce the bandwidth consumption. Extensive experiments conducted on a real world dataset have demonstrated that our approach obviously decreases the processing time, while improves the precision and recall as well.

A dynamic routing protocol for keyword search in unstructured peer-to-peer networks

The idea of building query-oriented routing indices has changed the way of improving keyword search efficiency from the basis as it can learn the content distribution from the query routing process. It gradually improves search efficiency without excessive network overhead for the construction and maintenance of routing indices. However, previously proposed protocol is not practically effective due to the slow improvement of routing efficiency. In this paper, we propose a novel protocol for query-oriented routing indices which quickly achieves high search efficiency at low cost. The maintenance mechanism employs reinforcement learning to exploit mass peer behavior. It explicitly uses the expected number of returned results to depict the content distribution, which helps quickly approximate the real distribution. The routing mechanism is to retrieve as many contents as possible and help speed up the learning process. To further improve the search efficiency, several methods are taken to optimize the routing and maintenance mechanism. In dealing with multi-keyword queries, the information of corresponding keywords is also used to forward the queries. In addition, to accelerate the learning speed, a rough description of content distribution is achieved when the query is first seen. The experimental evaluation shows that the mechanism achieves high routing efficiency, quick learning ability, and satisfactory performance under churn.