Efficient Expressive Attribute-Based Encryption with Keyword Search over Prime-Order Groups

Attribute-based encryption with keyword search(ABEKS)is a novel cryptographic paradigm that can be used to implementfine-grained access control and retrieve ciphertexts without disclosing the sensitive information.It is a perfect combination of attribute-based encryption(ABE)and public key encryption with keyword search(PEKS).Nevertheless,most of the existing ABEKS schemes have limited search capabilities and only support single or simple conjunctive keyword search.Due to the weak search capability and inaccurate search results,it is difficult to apply these schemes to practical applications.In this paper,an effi-cient expressive ABEKS(EABEKS)scheme supporting unbounded keyword uni-verse over prime-order groups is designed,which supplies the expressive keyword search function supporting the logical connectives of“AND”and“OR”.The proposed scheme not only leads to low computation and communica-tion costs,but also supports unbounded keyword universe.In the standard model,the scheme is proven to be secure under the chosen keyword attack and the cho-sen plaintext attack.The comparison analysis and experimental results show that it has better performance than the existing EABEKS schemes in the storage,com-putation and communication costs.

Hash Function Based Keyword Searchable Encryption Framework in Cloud Server Using MD5 and MECC

Cloud Computing expands its usability to various fields that utilize data and store it in a common space that is required for computing and the purpose of analysis as like the IoT devices.These devices utilize the cloud for storing and retrieving data since the devices are not capable of storing processing data on its own.Cloud Computing provides various services to the users like the IaaS,PaaS and SaaS.The major drawback that is faced by cloud computing include the Utilization of Cloud services for the storage of data that could be accessed by all the users related to cloud.The use of Public Key Encryptions with keyword search(PEKS)provides security against the untrustworthy third-party search capability on publicly encryption keys without revealing the data’s contents.But the Security concerns of PEKs arise when Inside Keywords Guessing attacks(IKGA),is identified in the system due to the untrusted server presume the keyword in trapdoor.This issue could be solved by using various algorithms like the Certificateless Hashed Public Key Authenticated Encryption with Keyword Search(CL-HPAEKS)which utilizes the Modified Elliptic Curve Cryptography(MECC)along with the Mutation Centred flower pollinations algorithm(CM-FPA)that is used in enhancing the performance of the algorithm using the Optimization in keys.The additional use of Message Digests 5(MD5)hash function in the system enhances the security Level that is associated with the system.The system that is proposed achieves the security level performance of 96 percent and the effort consumed by the algorithm is less compared to the other encryption techniques.

Keyword Extraction Based on tf/idf for Chinese News Document

Keyword extraction is an important research topic of information retrieval. This paper gave the specification of keywords in Chinese news documents based on analyzing linguistic characteristics of news documents and then proposed a new keyword extraction method based on tf/idf with multi-strategies. The approach selected candidate keywords of uni-, hi- and tri-grams, and then defines the features according to their morphological characters and context information. Moreover, the paper proposed several strategies to amend the incomplete words gotten from the word segmentation and found unknown potential keywords in news documents. Experimental results show that our proposed method can significantly outperform the baseline method. We also applied it to retrospective event detection. Experimental results show that the accuracy and efficiency of news retrospective event detection can be significantly improved.

KRBKSS: a keyword relationship based keyword-set search system for peer-to-peer networks

may incur significant bandwidth for executing more com- plicated search queries such as multiple-attribute queries. In order to reduce query overhead, KSS (keyword-set search) by Gnawali partitions the index by a set of keywords. However, a KSS index is considerably larger than a standard inverted index, since there are more word sets than there are individual words. And the insert overhead and storage overhead are obviously un- acceptable for full-text search on a collection of documents even if KSS uses the distance window technology. In this paper, we extract the relationship information between query keywords from websites’ queries logs to improve performance of KSS system. Experiments results clearly demonstrated that the improved keyword-set search system based on keywords relationship (KRBKSS) is more efficient than KSS index in insert overhead and storage overhead, and a standard inverted index in terms of communication costs for query.

Constructing Certificateless Encryption with Keyword Search against Outside and Inside Keyword Guessing Attacks

Searchable public key encryption is a useful cryptographic paradigm that enables an untrustworthy server to retrieve the encrypted data without revealing the contents of the data. It offers a promising solution to encrypted data retrieval in cryptographic cloud storage. Certificateless public key cryptography (CLPKC) is a novel cryptographic primitive that has many merits. It overcomes the key escrow problem in identity-based cryptography (IBC) and the cumbersome certificate problem in conventional public key cryptography (PKC). Motivated by the appealing features of CLPKC, several certificateless encryption with keyword search (CLEKS) schemes have been presented in the literature. But, our cryptanalysis demonstrates that the previously proposed CLEKS frameworks suffer from the security vulnerability caused by the keyword guessing attack. To remedy the security weakness in the previous frameworks and provide resistance against both inside and outside keyword guessing attacks, we propose a new CLEKS framework. Under the new framework, we design a concrete CLEKS scheme and formally prove its security in the random oracle model. Compared with previous two CLEKS schemes, the proposed scheme has better overall performance while offering stronger security guarantee as it withstands the existing known types of keyword guessing attacks.

Certificateless Public Key Encryption with Keyword Search

Public Key Encryption with Keyword Search （PEKS）, an indispensable part of searchable encryption, is stock-in- trade for both protecting data and providing operability of encrypted data. So far most of PEKS schemes have been established on Identity-Based Cryptography （IBC） with key escrow problem inherently. Such problem severely restricts the promotion of IBC-based Public Key Infrastructure including PEKS component. Hence, Certificateless Public Key Cryptography （CLPKC） is efficient to remove such problem. CLPKC is introduced into PEKS, and a general model of Certificateless PEKS （CLPEKS） is formalized. In addition, a practical CLPEKS scheme is constructed with security and efficiency analyses. The proposal is secure channel free, and semantically secure against adaptive chosen keyword attack and keyword guessing attack. To illustrate the superiority, massive experiments are conducted on Enron Email dataset which is famous in information retrieval field. Compared with existed constructions, CLPEKS improves the efficiency in theory and removes the key escrow problem.

Keyword Cloud在文献检索中的应用研究

理论部分对Keyword Cloud的来源、概念、功能以及在图书馆服务中的需求分析进行介绍,为实际应用奠定理论基础。实践部分将Keyword Cloud应用于文献数据库检索服务中,用于汇总检索结果和辅助二次检索;同时引入TagLine技术为传统Keyword Cloud增加时间框架,便于用户观察热点趋势变化;通过用户调查对Keyword Cloud的实用性和适用性进行评价;总结下一步工作的重点在于关键词语义关系的构建。

Lattice-Based Searchable Encryption Scheme against Inside Keywords Guessing Attack

To save the local storage,users store the data on the cloud server who offers convenient internet services.To guarantee the data privacy,users encrypt the data before uploading them into the cloud server.Since encryption can reduce the data availability,public-key encryption with keyword search(PEKS)is developed to achieve the retrieval of the encrypted data without decrypting them.However,most PEKS schemes cannot resist quantum computing attack,because the corresponding hardness assumptions are some number theory problems that can be solved efficiently under quantum computers.Besides,the traditional PEKS schemes have an inherent security issue that they cannot resist inside keywords guessing attack(KGA).In this attack,a malicious server can guess the keywords encapsulated in the search token by computing the ciphertext of keywords exhaustively and performing the test between the token and the ciphertext of keywords.In the paper,we propose a lattice-based PEKS scheme that can resist quantum computing attacks.To resist inside KGA,this scheme adopts a lattice-based signature technique into the encryption of keywords to prevent the malicious server from forging a valid ciphertext.Finally,some simulation experiments are conducted to demonstrate the performance of the proposed scheme and some comparison results are further shown with respect to other searchable schemes.

K-Gram Based Fuzzy Keyword Search over Encrypted Cloud Computing

With recent significant development in the portable device market, cloud computing is getting more and more utilized. Many sensitive data are stored in cloud central servers. To ensure privacy, these data are usually encrypted before being uploaded—making file searching complicated. Although previous cloud computing searchable encryption schemes allow users to search encrypted data by keywords securely, these techniques only support exact keyword search and will fail if there are some spelling errors or if some morphological variants of words are used. In this paper, we provide the solution for fuzzy keyword search over encrypted cloud data. K-grams is used to produce fuzzy results. For security reasons, we use two separate servers that cannot communicate with each other. Our experiment result shows that our system is effective and scalable to handle large number of encrypted files.

An improved algorithm for weighting keywords in web documents

In this paper, an improved algorithm, web-based keyword weight algorithm （WKWA）, is presented to weight keywords in web documents. WKWA takes into account representation features of web documents and advantages of the TF＊IDF, TFC and ITC algorithms in order to make it more appropriate for web documents. Meanwhile, the presented algorithm is applied to improved vector space model （IVSM）. A real system has been implemented for calculating semantic similarities of web documents. Four experiments have been carried out. They are keyword weight calculation, feature item selection, semantic similarity calculation, and WKWA time performance. The results demonstrate accuracy of keyword weight, and semantic similarity is improved.

A Metric Approach to Hot Topics in Biomedicine via Keyword Co-occurrence

Purpose:To reveal the research hotpots and relationship among three research hot topics in b iomedicine,namely CRISPR,iPS(induced Pluripotent Stem)cell and Synthetic biology.Design/methodology/approach:We set up their keyword co-occurrence networks with using three indicators and information visualization for metric analysis.Findings:The results reveal the main research hotspots in the three topics are different,but the overlapping keywords in the three topics indicate that they are mutually integrated and interacted each other.Research limitations:All analyses use keywords,without any other forms.Practical implications:We try to find the information distribution and structure of these three hot topics for revealing their research status and interactions,and for promoting biomedical developments.Originality/value:We chose the core keywords in three research hot topics in biomedicine by using h-index.

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.

Privacy-Preserving Top-k Keyword Similarity Search over Outsourced Cloud Data

In this paper,we study the problem of privacy-preserving top-k keyword similarity search over outsourced cloud data.Taking edit distance as a measure of similarity,we first build up the similarity keyword sets for all the keywords in the data collection.We then calculate the relevance scores of the elements in the similarity keyword sets by the widely used tf-idf theory.Leveraging both the similarity keyword sets and the relevance scores,we present a new secure and efficient treebased index structure for privacy-preserving top-k keyword similarity search.To prevent potential statistical attacks,we also introduce a two-server model to separate the association between the index structure and the data collection in cloud servers.Thorough analysis is given on the validity of search functionality and formal security proofs are presented for the privacy guarantee of our solution.Experimental results on real-world data sets further demonstrate the availability and efficiency of our solution.

WoS题录Keywords字段关键词频度统计程序设计与实现

使用Visual Basic编程,采用正则表达式批量提取由Web of Science导出的Bib Tex题录中所有Keywords字段关键词,按需合并所得关键词的同义词、近义词及词形变化词,然后将出现频度的统计数据写入Excel表,并编制Excel宏自动生成折线图,实现关键词分布的简单可视化。情报工作者后续可借助Excel功能对该程序生成的Excel表执行复杂的数据组合分析,以提高工作效率。

Incremental Training for SVM-Based Classification with Keyword Adjusting

This paper analyzed the theory of incremental learning of SVM (support vector machine) and pointed out it is a shortage that the support vector optimization is only considered in present research of SVM incremental learning. According to the significance of keyword in training, a new incremental training method considering keyword adjusting was proposed, which eliminates the difference between incremental learning and batch learning through the keyword adjusting. The experimental results show that the improved method outperforms the method without the keyword adjusting and achieve the same precision as the batch method. Key words SVM (support vector machine) - incremental training - classification - keyword adjusting CLC number TP 18 Foundation item: Supported by the National Information Industry Development Foundation of ChinaBiography: SUN Jin-wen (1972-), male, Post-Doctoral, research direction: artificial intelligence, data mining and system integration.