Weighted PageRank Algorithm Search Engine Ranking Model for Web Pages

As data grows in size,search engines face new challenges in extracting more relevant content for users’searches.As a result,a number of retrieval and ranking algorithms have been employed to ensure that the results are relevant to the user’s requirements.Unfortunately,most existing indexes and ranking algo-rithms crawl documents and web pages based on a limited set of criteria designed to meet user expectations,making it impossible to deliver exceptionally accurate results.As a result,this study investigates and analyses how search engines work,as well as the elements that contribute to higher ranks.This paper addresses the issue of bias by proposing a new ranking algorithm based on the PageRank(PR)algorithm,which is one of the most widely used page ranking algorithms We pro-pose weighted PageRank(WPR)algorithms to test the relationship between these various measures.The Weighted Page Rank(WPR)model was used in three dis-tinct trials to compare the rankings of documents and pages based on one or more user preferences criteria.Thefindings of utilizing the Weighted Page Rank model showed that using multiple criteria to rankfinal pages is better than using only one,and that some criteria had a greater impact on ranking results than others.

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.

Evaluation of the Importance of Web Pages

Google’s algorithm on PageRank is analyzed in details. Some disadvantages of this algorithm is presented, for instance, preferring old pages, ignoring special sites and inaccurate judge of hyperlinks pointed out from one page. Furthermore, author’s improved algorithm is described. Experiments show that the author’s consideration on evaluating the importance of pages can make an improvement over the original algorithm. Based on this improved algorithm a topic specific searching system have been developed.

Improved Research on Fuzzy Search over Encrypted Cloud Data Based on Keywords

A search strategy over encrypted cloud data based on keywords has been improved and has presented a method using different strategies on the client and the server to improve the search efficiency in this paper. The client uses the Chinese and English to achieve the synonym construction of the keywords, the establishment of the fuzzy-syllable words and synonyms set of keywords and the implementation of fuzzy search strategy over the encryption of cloud data based on keywords. The server side through the analysis of the user’s query request provides keywords for users to choose and topic words and secondary words are picked out. System will match topic words with historical inquiry in time order, and then the new query result of the request is directly gained. The analysis of the simulation experiment shows that the fuzzy search strategy can make better use of historical results on the basis of privacy protection for the realization of efficient data search, saving the search time and improving the efficiency of search.

Stability-mutation feature identification of Web search keywords based on keyword concentration change ratio

Purpose: The aim of this paper is to discuss how the keyword concentration change ratio(KCCR) is used while identifying the stability-mutation feature of Web search keywords during information analyses and predictions.Design/methodology/approach: By introducing the stability-mutation feature of keywords and its significance, the paper describes the function of the KCCR in identifying keyword stability-mutation features. By using Ginsberg's influenza keywords, the paper shows how the KCCR can be used to identify the keyword stability-mutation feature effectively.Findings: Keyword concentration ratio has close positive correlation with the change rate of research objects retrieved by users, so from the characteristic of the 'stability-mutation' of keywords, we can understand the relationship between these keywords and certain information. In general, keywords representing for mutation fit for the objects changing in short-term, while those representing for stability are suitable for long-term changing objects. Research limitations: It is difficult to acquire the frequency of keywords, so indexes or parameters which are closely related to the true search volume are chosen for this study.Practical implications: The stability-mutation feature identification of Web search keywords can be applied to predict and analyze the information of unknown public events through observing trends of keyword concentration ratio.Originality/value: The stability-mutation feature of Web search could be quantitatively described by the keyword concentration change ratio(KCCR). Through KCCR, the authors took advantage of Ginsberg's influenza epidemic data accordingly and demonstrated how accurate and effective the method proposed in this paper was while it was used in information analyses and predictions.

Searching Databases with Keywords

Traditionally, SQL query language is used to search the data in databases. However, it is inappropriate for end-users, since it is complex and hard to learn. It is the need of end-user, searching in databases with keywords, like in web search engines. This paper presents a survey of work on keyword search in databases. It also includes a brief introduction to the SEEKER system which has been developed.

面向连接关键词可搜索加密的查询恢复攻击

为了恢复连接关键词可搜索加密方案中的用户查询,提出了2种针对连接查询可搜索加密方案的攻击方法,分别是交叉泄露攻击和频率匹配攻击。首先,从泄露中提取候选关键词集合;然后,分别利用关键词对结果模式泄露和查询频率信息进行过滤。结果表明,在交叉泄露攻击中,当攻击者仅掌握10%的数据集时,若关键词在空间为100,查询恢复的准确率可高达90%,将关键词空间扩大至1000,攻击者依然能够恢复50%以上的查询;在频率匹配攻击中,即使攻击者仅已知不准确的频率分布信息,也至少可以准确恢复70%的查询。

基于区块链高效安全的多部门铁路工程数据访问控制策略

针对铁路工程建设中各参建部门之间的数据资源,具有共享效率差、利用率低和安全程度不高的问题,提出一种基于区块链(Blockchain)铁路工程数据的共享及安全存储方案来弥补现有的铁路工程数据平台的不足。在工程建设平台中,引入了基于外包的属性加密技术(Outsourcing-Attribute Based Encryption technology, OABE)、区块链技术和星际文件系统(Interplanetary File System, IPFS)以及边缘节点(Edge Nodes, EN),并提出一种适用于铁路工程建设的多部门区块链与IPFS的协作网络。研究结果表明:当施工现场资源设备有限时,相比于较为传统基于云辅助的密文策略属性基加密技术(Ciphertext Policy Attribute Based Encryption, CP-ABE),通过将铁路工程数据外包给边缘节点,可以更好地为资源受限设备提供大量的计算,减轻设备的计算负担,确保了数据的安全性并且提高了设备的计算效率。而且将铁路工程数据存储在星际文件系统上,避免集中式的服务器机制在故障时造成数据的丢失和泄露。此外,通过使用区块链网络可以提高参建单位之间的数据资源共享效率,增强了数据的利用率,利用区块链的共识性来实现每个参建单位之间数据的一致性,确保了不同参建单位间的信任度。经过安全性证明,采用基于双线性迪菲赫尔曼指数假设(Decision q-Bilinear Diffie Hellman Exponent Assumption, q-BDHE)下具有不可区分性是安全的。实验结果表明,该策略在本地用户计算时间效率上与现有的带关键字搜索的外包属性基加密方案以及属性基加密的方案相比是最优的,可以很好地减轻现场设备的计算负担。

支持模糊多关键字的动态可搜索加密方案

随着云计算的发展,云存储的便捷性和经济性使得大量用户将个人数据存储在第三方云服务器上.云端数据的加密存储可确保数据的安全性,但也增加了数据检索的难度.动态对称可搜索加密技术应运而生,其不仅能有效保护数据隐私,还可实现多关键字联合搜索的功能.同时,该技术在实际应用中引入的模糊搜索还可以增强用户的搜索体验和提高搜索效率.当前支持模糊搜索的可搜索加密方案存在一定的安全隐患,其未考虑动态更新时潜在的信息泄露问题.为此,提出了一种支持模糊多关键字的动态可搜索加密方案,在保证动态更新时信息安全的同时,支持多关键字联合搜索以及模糊搜索.该方案设计了一种关键字编码算法和局部敏感哈希函数构建模糊索引,并使用布隆过滤器加密算法对该索引进行加密,以实现模糊搜索.此外,引入了可信执行环境降低用户和服务器之间的通信开销、计算开销以及交互次数.最后,通过实验验证了该方案的安全性和有效性.

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。

对网页PageRank算法的改进

分析了著名搜索引擎 Google采用的 Page Rank算法 ,指出其偏重旧网页、忽视专业站点以及对网页中的超链接评估不恰当等不足之处 .改进算法考虑了网页日期这一重要因素 ,并重新计算网页中超链接对网页的影响 .网页结构中蕴涵着丰富的信息 ,在 href、title等标记中文字对网页主题有重要作用 ,利用结构标记可以辅助判断网页的主题内容 .试验结果表明 。

基于PageRank的Web挖掘改进算法

针对Google使用的PageRank算法,提出一种改进的Web挖掘算法。实现该算法时,将网页使用信息和网页添加日期信息做成点击向量和日期向量,2个向量加权后标准化得到的一个向量作为常数加入到改进的迭代算法。实验结果证明,改进算法可以提高网页重要性判断的准确度。

基于用户兴趣度的PageRank改进算法

传统的PageRank算法容易导致主题漂移、偏重旧网页、用户对搜索结果的主观选择被忽略等问题。针对PageRank算法存在的上述缺陷,提出了一种基于用户兴趣度的网页排序算法——PRUI算法。该算法主要从网页自身的客观特性和用户兴趣的主观特性两方面对网页的PR值进行重新估算,并依据估算后的网页PR值对网页做重排序。相比传统的PageRank算法,改进的PRUI算法进一步提高了系统检索的准确率和首页命中率。

支持语义扩展的多关键词密文检索方案

现有的多关键词的检索研究集中在支持关键词的精确匹配,文中提出一个支持语义扩展查询的多关键词密文检索方案,基于信息内容计算方法同时考虑到关键词与文档的关系引入TF-IDF技术实现语义扩展;采用平衡二叉树的索引结构,使用安全k近邻算法加密索引向量和语义扩展查询向量,确保计算出准确的相关性得分,实现多关键词排序搜索。通过进行索引和陷门的机密性、查询无关联性等安全性分析表明,本文实现的检索方案是安全的,可以防止内部威胁。

支持关键词搜索的top-K条最优路线查询问题

支持关键词搜索的top-K条最优路线查询问题是针对给定的道路网络、兴趣点集合、起点和多个关键词的路线查询。查询旨在找到途经与查询关键词匹配的多个兴趣点的k条最优路线。然而,一些现有研究为降低算法的复杂度,将用户输入关键词的顺序作为到达兴趣点顺序,不适用于对兴趣点到达顺序没有要求的场景,降低了实用性;另一些研究为提高查询效率,设定距离阈值对不符合要求的兴趣点剪枝,然而这类算法无法保证被剪枝的兴趣点一定不能组成最优路线。针对上述问题,提出一种关键词感知的top-K最优路线搜索(KKRS)算法。首先,将整个道路网络划分为多个子网络。然后,采用启发式搜索策略从查询起点所在的子网络开始逐步扩展搜索范围,直至找到top-K条最优路线或遍历完整个道路网络。在扩展过程中,引入子图剪枝策略,用于剪去不包含top-K最优路线的子网络,缩小搜索范围。此外,为避免对每个可能构成最优路线的兴趣点集合依次计算,设计兴趣点序列的剪枝策略,以快速过滤不可能构成最优路线的兴趣点序列,降低计算代价。最后,在真实数据集和合成数据集上对提出的两种剪枝算法进行实验,实验结果表明这两种算法在所有数据集上的子图剪枝上都能达到70%以上的剪枝率,在兴趣点序列剪枝上则能保证60%以上的剪枝率。与目前已有的先进算法大规模路网图下关键词覆盖最优路径查询(KORL)、ROSE-GM(Recurrent Optimal Subroute Expansion Using Greedy Merge Strategy)、OSSCaling和StarKOSR(finding Top-K Optional Sequenced Routes with A*)相比,KKRS算法比对比算法中查询效率较高的StarKOSR算法提高了40%。

基于时间的关键字路网路径规划

为了便于道路网距离的计算、对象文本信息和时间信息的组织和快速获取,改进G-Tree,在索引结构上增加时间戳和倒排序列相关信息,称包含时间戳和倒排序列相关信息的G-Tree为IGT-Tree.并基于构建的IGT-Tree设计了两种推荐算法:一种是基于时间的目标点查询算法,另一种是在目标点查询算法基础上的路径推荐算法.最后,在基于时间的关键字路网通过实验对比G-Tree的算法验证了所提算法的高效性.

基于内容相关性和时间分析的改进PageRank算法

传统PageRank算法仅仅通过网页链接结构来确定网页的PageRank值,这通常会导致主题漂移问题,即主题内容不相关的网页PageRank值较高。改进的PageRank算法根据链接的网页之间的内容相关性分配rank值,并且将网页的时间因素考虑在内,提高新的内容相关网页的rank值。实验结果证明,改进后的算法有效解决原算法的主题漂移问题,并使新网页在排序结果中上升。

数据隐私保护关键词检索技术研究综述与应用分析

在现代信息社会,数据隐私保护成为公众关注的焦点。随着互联网用户对个人信息安全的日益重视,信息检索领域的隐私保护研究变得至关重要。隐私保护关键词检索技术旨在在不泄露用户查询意图的情况下,提供安全、保护隐私的检索服务。尽管现有技术在满足基本需求方面取得了进展,但如何在保持效率的同时减少隐私泄露风险,仍是一个挑战。为此,本文对隐私保护关键词检索技术进行了详细回顾,系统地分析了当前主流技术的原理、优势与不足。研究发现,尽管已有技术能够对用户查询进行加密处理,防止敏感信息直接泄露,但在查询模式、访问模式与返回结果之间,仍存在着潜在的隐私泄露风险。针对这一问题,本文提出了一系列改进方向,以增强隐私保护的效果。此外,当前的隐私保护技术在实际应用中面临诸多挑战,这些挑战涉及技术增强、隐私合规等多个方面。通过对隐私保护关键词检索相关前沿技术的融合与创新,有望为解决这些技术问题提供新的思路和方案,推动隐私保护技术向更高水平发展。本文最后对隐私保护关键词检索技术的未来发展方向和创新应用模式进行了展望。

基于字典分级和属性加权的密文排序检索方案

可搜索加密支持用户在不解密原始数据的前提下对加密数据执行检索操作.现有的多关键词排序可搜索加密方案,其索引和陷门构建的时间成本通常依赖于由全局关键词字典张成的向量空间.为了减少用户端的计算开销和通信成本,进一步提升数据使用者对检索结果的满意度,提出了一种支持细粒度访问控制的多关键词密文排序检索方案.该方案首先设计基于互信息的字典剥离机制差异化全局字典中的关键词,得到两个信息量不同的附属子字典,进一步在低维子字典空间上生成索引和陷门;其次,引入文档访问策略中属性的权重,将其作为排序标准之一,使数据使用者获得更为相关的结果;最后,检索时利用筛选向量对数据进行初次过滤并借助属性匹配完成二次剔除,从而避免检索过程中不必要的计算.

A Lightweight, Searchable, and Controllable EMR Sharing Scheme

Electronic medical records (EMR) facilitate the sharing of medical data, but existing sharing schemes suffer fromprivacy leakage and inefficiency. This article proposes a lightweight, searchable, and controllable EMR sharingscheme, which employs a large attribute domain and a linear secret sharing structure (LSSS), the computationaloverhead of encryption and decryption reaches a lightweight constant level, and supports keyword search andpolicy hiding, which improves the high efficiency of medical data sharing. The dynamic accumulator technologyis utilized to enable data owners to flexibly authorize or revoke the access rights of data visitors to the datato achieve controllability of the data. Meanwhile, the data is re-encrypted by Intel Software Guard Extensions(SGX) technology to realize resistance to offline dictionary guessing attacks. In addition, blockchain technology isutilized to achieve credible accountability for abnormal behaviors in the sharing process. The experiments reflectthe obvious advantages of the scheme in terms of encryption and decryption computation overhead and storageoverhead, and theoretically prove the security and controllability in the sharing process, providing a feasible solutionfor the safe and efficient sharing of EMR.