Privacy-Preserving Multi-Keyword Fuzzy Adjacency Search Strategy for Encrypted Graph in Cloud Environment

In a cloud environment,outsourced graph data is widely used in companies,enterprises,medical institutions,and so on.Data owners and users can save costs and improve efficiency by storing large amounts of graph data on cloud servers.Servers on cloud platforms usually have some subjective or objective attacks,which make the outsourced graph data in an insecure state.The issue of privacy data protection has become an important obstacle to data sharing and usage.How to query outsourcing graph data safely and effectively has become the focus of research.Adjacency query is a basic and frequently used operation in graph,and it will effectively promote the query range and query ability if multi-keyword fuzzy search can be supported at the same time.This work proposes to protect the privacy information of outsourcing graph data by encryption,mainly studies the problem of multi-keyword fuzzy adjacency query,and puts forward a solution.In our scheme,we use the Bloom filter and encryption mechanism to build a secure index and query token,and adjacency queries are implemented through indexes and query tokens on the cloud server.Our proposed scheme is proved by formal analysis,and the performance and effectiveness of the scheme are illustrated by experimental analysis.The research results of this work will provide solid theoretical and technical support for the further popularization and application of encrypted graph data processing technology.

Ranking of Web Pages in a Personalized Search

The basic idea behind a personalized web search is to deliver search results that are tailored to meet user needs, which is one of the growing concepts in web technologies. The personalized web search presented in this paper is based on exploiting the implicit feedbacks of user satisfaction during her web browsing history to construct a user profile storing the web pages the user is highly interested in. A weight is assigned to each page stored in the user’s profile;this weight reflects the user’s interest in this page. We name this weight the relative rank of the page, since it depends on the user issuing the query. Therefore, the ranking algorithm provided in this paper is based on the principle that;the rank assigned to a page is the addition of two rank values R_rank and A_rank. A_rank is an absolute rank, since it is fixed for all users issuing the same query, it only depends on the link structures of the web and on the keywords of the query. Thus, it could be calculated by the PageRank algorithm suggested by Brin and Page in 1998 and used by the google search engine. While, R_rank is the relative rank, it is calculated by the methods given in this paper which depends mainly on recording implicit measures of user satisfaction during her previous browsing history.

Verifiable Diversity Ranking Search Over Encrypted Outsourced Data

Data outsourcing has become an important application of cloud computing.Driven by the growing security demands of data outsourcing applications,sensitive data have to be encrypted before outsourcing.Therefore,how to properly encrypt data in a way that the encrypted and remotely stored data can still be queried has become a challenging issue.Searchable encryption scheme is proposed to allow users to search over encrypted data.However,most searchable encryption schemes do not consider search result diversification,resulting in information redundancy.In this paper,a verifiable diversity ranking search scheme over encrypted outsourced data is proposed while preserving privacy in cloud computing,which also supports search results verification.The goal is that the ranked documents concerning diversification instead of reading relevant documents that only deliver redundant information.Extensive experiments on real-world dataset validate our analysis and show that our proposed solution is effective for the diversification of documents and verification.

Secure Multi-keyword Search Supporting Dynamic Update and Ranked Retrieval

As cloud computing is becoming prevalent, data owners are motivated to delegate complex data managements to the commercial cloud for economic savings. Sensitive data is usually encrypted before being uploaded to the cloud, which unfortunately makes the frequently-used search function a challenging problem. In this paper, we present a new multi-keyword dynamic search scheme with result ranking to make search over encrypted data more secure and practical. In the scheme, we employ a powerful function-hiding inner product encryption to enhance the security by preventing the leakage of search pattern. For the concern of efficiency, we adopt a tree-based index structure to facilitate the searching process and updating operations. A comprehensive security analysis is provided and experiments over the real world data show that our scheme is efficient.

An Ontology-based Ranking Model in Search Engines

As the tsunami of data has emerged,search engines have become the most powerful tool for obtaining scattered information on the internet.The traditional search engines return the organized results by using ranking algorithm such as term frequency,link analysis(PageRank algorithm and HITS algorithm)etc.However,these algorithms must combine the keyword frequency to determine the relevance between user’s query and the data in the computer system or internet.Moreover,we expect the search engines could understand users’searching by content meanings rather than literal strings.Semantic Web is an intelligent network and it could understand human’s language more semantically and make the communication easier between human and computers.But,the current technology for the semantic search is hard to apply.Because some meta data should be annotated to each web pages,then the search engine will have the ability to understand the users intend.However,annotate every web page is very time-consuming and leads to inefficiency.So,this study designed an ontology-based approach to improve the current traditional keyword-based search and emulate the effects of semantic search.And let the search engine can understand users more semantically when it gets the knowledge.

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.

Authorized Attribute-Based Encryption Multi-Keywords Search with Policy Updating

Attribute-based encryption is cryptographic techniques that provide flexible data access control to encrypted data content in cloud storage.Each trusted authority needs proper management and distribution of secret keys to the user’s to only authorized user’s attributes.However existing schemes cannot be applied multiple authority that supports only a single keywords search compare to multi keywords search high computational burden or inefficient attribute’s revocation.In this paper,a ciphertext policy attribute-based encryption(CP-ABE)scheme has been proposed which focuses on multi-keyword search and attribute revocation by new policy updating feathers under multiple authorities and central authority.The data owner encrypts the keywords index under the initial access policy.Moreover,this paper addresses further issues such as data access,search policy,and confidentiality against unauthorized users.Finally,we provide the correctness analysis,performance analysis and security proof for chosen keywords attack and search trapdoor in general group model using DBDH and DLIN assumption.

3-Multi ranked encryption with enhanced security in cloud computing

Searchable Encryption(SE)enables data owners to search remotely stored ciphertexts selectively.A practical model that is closest to real life should be able to handle search queries with multiple keywords and multiple data owners/users,and even return the top-k most relevant search results when requested.We refer to a model that satisfies all of the conditions a 3-multi ranked search model.However,SE schemes that have been proposed to date use fully trusted trapdoor generation centers,and several methods assume a secure connection between the data users and a trapdoor generation center.That is,they assume the trapdoor generation center is the only entity that can learn the information regarding queried keywords,but it will never attempt to use it in any other manner than that requested,which is impractical in real life.In this study,to enhance the security,we propose a new 3-multi ranked SE scheme that satisfies all conditions without these security assumptions.The proposed scheme uses randomized keywords to protect the interested keywords of users from both outside adversaries and the honest-but-curious trapdoor generation center,thereby preventing attackers from determining whether two different queries include the same keyword.Moreover,we develop a method for managing multiple encrypted keywords from every data owner,each encrypted with a different key.Our evaluation demonstrates that,despite the trade-off overhead that results from the weaker security assumption,the proposed scheme achieves reasonable performance compared to extant schemes,which implies that our scheme is practical and closest to real life.

Research on Fuzzy Matching Query Algorithm Based on Spatial Multi-keyword

With the rapid growth of spatial data,POI(Point of Interest)is becoming ever more intensive,and the text description of each spatial point is also gradually increasing.The traditional query method can only address the problem that the text description is less and single keyword query.In view of this situation,the paper proposes an approximate matching algorithm to support spatial multi-keyword.The fuzzy matching algorithm is integrated into this algorithm,which not only supports multiple POI queries,but also supports fault tolerance of the query keywords.The simulation results demonstrate that the proposed algorithm can improve the accuracy and efficiency of query.

基于联盟区块链和星际文件系统的安全租房方案

近年来租房市场蓬勃发展,各种租房平台应运而生。但现有的租房平台中心化问题严重,一旦中心服务器遭到破坏,容易引起单点失败问题,同时泄露用户的隐私。联盟区块链以分布式方式存储数据和执行合约,不依赖单个节点的可用性,即使某个中心节点失效,其他节点仍然可以继续运行和验证交易,确保系统的持续稳定性和安全性。提出一个基于联盟区块链和星际文件系统(IPFS)的安全租房方案。在方案中,房东通过预设访问策略对房源数据进行属性基加密,以实现数据的机密性和安全的细粒度访问控制。采用多关键词排序搜索,只返回最符合用户需求的前k个文件,从而提高搜索精度,同时节省网络带宽资源。最后,在区块链节点上部署搜索智能合约和验证智能合约用于陷门与索引的匹配以及搜索结果正确性的检验,同时为用户引入信誉智能合约和支付智能合约,前者用于量化信誉度,后者用于实现自动化执行租赁合约。安全性证明和性能分析表明,该方案达到了选择明文安全性及较高的搜索效率。

基于语义排序功能实现对比文件快速筛选

为提高专利的审查质量和效率,本文提出了在智能检索系统下灵活运用语义排序功能以实现对比文件的快速筛选。通过结合一个实际案例的检索分析,阐述了如何在检索结果中运用语义排序功能。采用申请号或原权利要求作为一次语义排序基准,并以发明构思作为二次语义排序基准,可以将目标文献的排序提前,有效提高筛选效率。

多策略改进的蜣螂优化算法及其应用

蜣螂优化算法(DBO)是近年提出的智能优化算法,与其他优化算法一样,DBO也存在收敛精度低、易陷入局部最优等缺点。针对DBO的这些局限性,提出一种多策略改进的蜣螂优化算法(MIDBO)。首先,改进雏球和偷窃蜣螂对局部最优解和全局最优解的接受程度,使其根据自身搜索能力动态变化,既提升了种群质量又保持了适应度高的个体的良好搜索能力;其次,融合麻雀搜索算法中的追随者位置更新机制对算法进行扰动,并用贪婪策略更新位置,提升了算法的收敛精度;最后,当算法陷入停滞时引入柯西高斯变异策略,提高了算法跳出局部最优解的能力。仿真实验基于20个基准测试函数和CEC2019测试函数,验证了3种改进策略的有效性,将所改进算法和对比算法的优化结果进行收敛性分析和Wilcoxon秩和检验,证明了MIDBO具有良好的寻优性能和鲁棒性。将MIDBO运用在汽车碰撞优化问题的求解上,进一步验证了MIDBO在求解实际工程问题中的有效性和可靠性。

PageRank算法研究

深入剖析了著名搜索引擎Google的关键技术PageRank算法,介绍分析了该算法的当前发展现状。并针对PageRank算法容易出现主题漂移现象,利用提出的二阶相似度改进算法。实验表明,改进的算法有利于减少主题漂移现象,提高用户对检索结果的满意度。

搜索引擎平台优化策略何以提升消费者福利?

本文通过构造搜索引擎平台优化策略的一般性分析框架,及与之相匹配的两阶段理论模型,考察搜索引擎平台优化策略的作用机理,探索搜索引擎平台优化策略对消费者福利及搜索引擎平台利润的影响,从而推动搜索引擎平台优化策略的有效应用。研究发现:短期来看,搜索引擎平台优化策略的排序均衡将违背消费者检索意愿,虽激励了低质量销售商参与付费位置拍卖活动,维持付费位置拍卖市场稳定性,增加搜索引擎平台利润,却损害了消费者福利;长期来看,搜索引擎平台优化策略的排序均衡将契合消费者检索意愿,激励高质量销售商最大限度吸引消费者注意力,推动消费者实施购买行为,提升消费者福利,并通过降低均衡投标金额,一定程度地激励低质量销售商持续参与付费位置拍卖活动,进而实现协调双边用户的需求。并且,搜索引擎平台优化策略的排序均衡对搜索引擎平台利润有“先降后升”的“U”型曲线的影响。产业实践案例显示,中文主流搜索引擎平台的优化策略遵循长短期效应。

基于PageRank的页面排序改进算法

首先对PageRank算法进行了一般性介绍,研究了现有的基于链接结构的改进算法。在此基础上,指出PageRank算法给不同网页分配相同的PageRank值影响了网页的排序质量,提出了一种基于多层分类技术的改进算法HCPR,并对PageRank和HCPR算法进行了相应测试和比较。实验结果表明,HCPR的排序结果比PageRank提高了约15.3%的相关度。

HotRank:热度敏感的非结构化数据检索排名算法

为满足用户对非结构化数据检索的需求,分析用户对数据的操作行为,提出一种新型的数据热度敏感的非结构化数据检索排名算法HotRank。通过对数据操作情况(任务、访问次数、编辑时长等)进行日志记录,形成非结构化数据检索数据集。在此基础上,定义数据的任务相似度和数据热度计算方法实现该算法。结合实例仿真,对算法进行评估,并将仿真结果与其他算法进行比较,证明了该排名算法的准确率优于其他算法。

Method of acquiring web features and its application in web search

Focusing on the problem that it is hard to utilize the web multi-fields information with various forms in large scale web search,a novel approach,which can automatically acquire features from web pages based on a set of well defined rules,is proposed.The features describe the contents of web pages from different aspects and they can be used to improve the ranking performance for web search.The acquired feature has the advantages of unified form and less noise,and can easily be used in web page relevance ranking.A special specs for judging the relevance between user queries and acquired features is also proposed.Experimental results show that the features acquired by the proposed approach and the feature relevance specs can significantly improve the relevance ranking performance for web search.

搜索引擎PageRank算法研究

在研究搜索引擎关键技术的基础上,深入细致地剖析了著名搜索引擎Google的链接分析排名算法PageRank算法,解说其运行原理并给出改进算法。在此基础上指出PageRank算法存在的缺陷,从而针对性地提出了优化方案,实验结果表明,改进的算法有利于提高算法的运行效率,提高用户对检索结果的查准率。

一种基于PageRank的页面排序改进算法

首先对PageRank算法进行了分析,探讨了国外现有的基于链接结构的改进算法.在此基础上,指出PageRank算法平均分配PageRank值影响了网页的排序质量,提出了一种不平均分配PageRank值的改进算法NPR,并对改进算法进行测试及分析.实验证明,NPR的排序结果比PageRank提高了约11.2%的相关度.

高效个性化PageRank算法综述

对高效的个性化PageRank算法进行综述,从个性化程度、可扩展性、计算效率和精确度等方面对部分个性化算法、可扩展的PPV算法和混合算法等3类算法进行了详细分析和比较,并讨论了目前工作中的不足及未来的研究方向。