VKFQ:A Verifiable Keyword Frequency Query Framework with Local Differential Privacy in Blockchain

With its untameable and traceable properties,blockchain technology has been widely used in the field of data sharing.How to preserve individual privacy while enabling efficient data queries is one of the primary issues with secure data sharing.In this paper,we study verifiable keyword frequency(KF)queries with local differential privacy in blockchain.Both the numerical and the keyword attributes are present in data objects;the latter are sensitive and require privacy protection.However,prior studies in blockchain have the problem of trilemma in privacy protection and are unable to handle KF queries.We propose an efficient framework that protects data owners’privacy on keyword attributes while enabling quick and verifiable query processing for KF queries.The framework computes an estimate of a keyword’s frequency and is efficient in query time and verification object(VO)size.A utility-optimized local differential privacy technique is used for privacy protection.The data owner adds noise locally into data based on local differential privacy so that the attacker cannot infer the owner of the keywords while keeping the difference in the probability distribution of the KF within the privacy budget.We propose the VB-cm tree as the authenticated data structure(ADS).The VB-cm tree combines the Verkle tree and the Count-Min sketch(CM-sketch)to lower the VO size and query time.The VB-cm tree uses the vector commitment to verify the query results.The fixed-size CM-sketch,which summarizes the frequency of multiple keywords,is used to estimate the KF via hashing operations.We conduct an extensive evaluation of the proposed framework.The experimental results show that compared to theMerkle B+tree,the query time is reduced by 52.38%,and the VO size is reduced by more than one order of magnitude.

Learned Distributed Query Optimizer:Architecture and Challenges

The query processing in distributed database management systems(DBMS)faces more challenges,such as more operators,and more factors in cost models and meta-data,than that in a single-node DMBS,in which query optimization is already an NP-hard problem.Learned query optimizers(mainly in the single-node DBMS)receive attention due to its capability to capture data distributions and flexible ways to avoid hard-craft rules in refinement and adaptation to new hardware.In this paper,we focus on extensions of learned query optimizers to distributed DBMSs.Specifically,we propose one possible but general architecture of the learned query optimizer in the distributed context and highlight differences from the learned optimizer in the single-node ones.In addition,we discuss the challenges and possible solutions.

Exploring features for automatic identification of news queries through query logs

Purpose:Existing researches of predicting queries with news intents have tried to extract the classification features from external knowledge bases,this paper tries to present how to apply features extracted from query logs for automatic identification of news queries without using any external resources.Design/methodology/approach:First,we manually labeled 1,220 news queries from Sogou.com.Based on the analysis of these queries,we then identified three features of news queries in terms of query content,time of query occurrence and user click behavior.Afterwards,we used 12 effective features proposed in literature as baseline and conducted experiments based on the support vector machine(SVM)classifier.Finally,we compared the impacts of the features used in this paper on the identification of news queries.Findings:Compared with baseline features,the F-score has been improved from 0.6414 to0.8368 after the use of three newly-identified features,among which the burst point(bst)was the most effective while predicting news queries.In addition,query expression(qes)was more useful than query terms,and among the click behavior-based features,news URL was the most effective one.Research limitations:Analyses based on features extracted from query logs might lead to produce limited results.Instead of short queries,the segmentation tool used in this study has been more widely applied for long texts.Practical implications:The research will be helpful for general-purpose search engines to address search intents for news events.Originality/value:Our approach provides a new and different perspective in recognizing queries with news intent without such large news corpora as blogs or Twitter.

Query Optimization Framework for Graph Database in Cloud Dew Environment

The query optimizer uses cost-based optimization to create an execution plan with the least cost,which also consumes the least amount of resources.The challenge of query optimization for relational database systems is a combinatorial optimization problem,which renders exhaustive search impossible as query sizes rise.Increases in CPU performance have surpassed main memory,and disk access speeds in recent decades,allowing data compression to be used—strategies for improving database performance systems.For performance enhancement,compression and query optimization are the two most factors.Compression reduces the volume of data,whereas query optimization minimizes execution time.Compressing the database reduces memory requirement,data takes less time to load into memory,fewer buffer missing occur,and the size of intermediate results is more diminutive.This paper performed query optimization on the graph database in a cloud dew environment by considering,which requires less time to execute a query.The factors compression and query optimization improve the performance of the databases.This research compares the performance of MySQL and Neo4j databases in terms of memory usage and execution time running on cloud dew servers.

Application of Recursive Query on Structured Query Language Server

The advantage of recursive programming is that it is very easy to write and it only requires very few lines of code if done correctly.Structured query language(SQL)is a database language and is used to manipulate data.In Microsoft SQL Server 2000,recursive queries are implemented to retrieve data which is presented in a hierarchical format,but this way has its disadvantages.Common table expression(CTE)construction introduced in Microsoft SQL Server 2005 provides the significant advantage of being able to reference itself to create a recursive CTE.Hierarchical data structures,organizational charts and other parent-child table relationship reports can easily benefit from the use of recursive CTEs.The recursive query is illustrated and implemented on some simple hierarchical data.In addition,one business case study is brought forward and the solution using recursive query based on CTE is shown.At the same time,stored procedures are programmed to do the recursion in SQL.Test results show that recursive queries based on CTEs bring us the chance to create much more complex queries while retaining a much simpler syntax.

Visible-infrared person re-identification using query related cluster

Visible-infrared person re-identification(VIPR), is a cross-modal retrieval task that searches a target from a gallery captured by cameras of different spectrums.The severe challenge for VIPR is the large intra-class variation caused by the modal discrepancy between visible and infrared images.For that, this paper proposes a query related cluster(QRC) method for VIPR.Firstly, this paper uses an attention mechanism to calculate the similarity relation between a visible query and infrared images with the same identity in the gallery.Secondly, those infrared images with the same query images are aggregated by using the similarity relation to form a dynamic clustering center corresponding to the query image.Thirdly, QRC loss function is designed to enlarge the similarity between the query image and its dynamic cluster center to achieve query related clustering, so as to compact the intra-class variations.Consequently, in the proposed QRC method, each query has its own dynamic clustering center, which can well characterize intra-class variations in VIPR.Experimental results demonstrate that the proposed QRC method is superior to many state-of-the-art approaches, acquiring a 90.77% rank-1 identification rate on the RegDB dataset.

面向云边端协同的多模态数据建模技术及其应用

云边端协同架构中数据类型多样,各级存储资源与计算资源存在差异,给数据管理带来新的挑战.现有数据模型或者数据模型的简单叠加,都难以同时满足云边端中多模态数据管理和协同管理需求.因此,研究面向云边端协同的多模态数据建模技术成为重要问题.其核心在于,如何高效地从云边端三层架构中得到满足应用所需的查询结果.从云边端三层数据的数据类型出发,提出了面向云边端协同的多模态数据建模技术,给出了基于元组的多模态数据模型定义,设计了6种基类,解决多模态数据统一表征困难的问题;提出了云边端协同查询的基本数据操作体系,以满足云边端业务场景的查询需求;给出了多模态数据模型的完整性约束,为查询优化奠定了理论基础.最后,给出了面向云边端协同多模态数据模型的示范应用,并从数据存储时间、存储空间和查询时间这3个方面对所提出的数据模型存储方法进行了验证.实验结果表明,所提方案能够有效地表示云边端协同架构中的多模态数据.

基于知识图谱的番茄种植管理可视化查询

为提高获取番茄种植管理知识的速度与准确率,研究了以图形式描述番茄在不同环境的种植管理,并基于知识图谱构建了番茄种植管理可视化查询系统。该方法利用“自顶向下”和“自底向上”的模块化CREATE解决了Neo4j的缓慢和准确率问题,并利用PyQt框架构建可视化查询界面,通过问题预处理和语义相似度计算输出最合适的番茄种植管理知识。试验结果表明:该方法的平均响应时间和平均准确率比Cypher查询语言分别提高88.33%及1.97%,可操性比Cypher语言友好。研究结果可以在不同环境下为番茄生产管理提供高质量的种植管理建议。

基于多级索引表的金融业务数据库精准查询方法

为提升金融业务数据库查询精准度和效率,设计了一种基于多级索引表的金融业务数据库精准查询方法。首先,构建多级索引的逻辑结构,设计多级索引表构建过程;其次,进行金融业务数据特征融合,为金融业务数据查询准备好数据条件;最后,设计金融业务数据库精准查询算法,完成基于多级索引表的金融业务数据库精准查询方法设计的全过程。实验结果表明:设计方法的金融业务数据库查询精准度均在90%以上,最高可达到98%,而且查询所需时间最短仅为5.25 s。本设计方法的适用性极强,具有较好的应用前景,以期能够为金融业务数据的精准高效查询提供参考借鉴。

车联网POI查询中的位置隐私和查询隐私联合保护机制

在车联网中,基于位置的服务(LBS)的兴趣点(POI)查询被广泛用于车载应用中。但是,由于攻击者容易获取车辆位置、查询内容以及其它额外信息,单独对位置隐私或查询隐私进行保护很难保障车载用户的隐私安全,使得对位置隐私和查询隐私开展联合保护越发关键。为此,该文提出一种基于虚拟序列的位置隐私和查询隐私联合保护机制。首先根据POI查询的限制,分析位置隐私和查询隐私的相关性,运用欧几里得距离和关联规则算法对其建模描述,得到相关性判断模型;然后基于虚拟序列,根据影响隐私保护的因素和真实查询的相关性值,将联合保护转化为虚拟序列的选择问题,建立联合保护优化模型,得到匿名程度高且匿名区域大的匿名查询集,防止攻击者识别出真实查询。最后,实验结果表明,与现有方案相比,所提联合保护机制能抵御针对位置隐私和查询隐私的联合攻击(语义范围攻击、时间关联攻击和长期观察攻击),能更有效地保护用户的LBS隐私。

正确性可验证的密文图数据最短路径外包计算方案

地理位置、社交网络等海量图数据应用广泛且包含大量隐私,通常需要安全的外包计算来提供多样化的查询服务。然而,如何设计正确性可验证的图数据外包计算协议仍是公开的难题。为此,提出了加密图数据上正确性可验证的精确最短路径外包计算方案。该方案利用加法同态加密构造密态图数据上的广度优先最短路径计算算法,支持加密图数据的精确最短距离查询外包计算;其次,基于双线性映射累加器构造最短路径外包计算结果的概率正确性验证机制。分析和证明表明,该方案能以概率可靠性实现正确性可验证的精确最短路径的外包计算,具备随机预言模型下的IND-CCA2安全。对比实验结果表明,所提方案相比其他相关方案在安全性、功能性方面有显著优势,性能上较已有可验证图数据外包计算方案在初始化及加密环节、查询环节、验证及解密环节的时间开销分别降低了0.15%~23.19%,12.91%~30.89%和1.13%~18.62%。

集合空间关键字内聚组查询方法

给定一个道路网络和社交网络,集合空间关键字查询的目的是找到一组兴趣点,该组兴趣点的文本信息包含所有查询关键字,与查询的位置较近且彼此之间的距离较小。内聚组查询的目的是找到在地理位置和社交关系上紧密联系的一组用户;而集合空间关键字内聚组查询的目的是找到满足查询要求的一对最佳匹配的兴趣点集合和用户集合。针对这一问题,提出一种新的集合空间关键字内聚组查询处理模式。首先通过快速贪心查询过程获得候选兴趣点集合,然后使用core-tree结构存储(k,c)-core核心分解的结果,从而提高内聚组查询效率,并且保证查询结果能够同时满足用户之间的社会关系约束和兴趣点之间的空间位置约束。通过在真实数据集上开展实验,结果表明提出的方法比枚举方法的查询效率快1~2个数量级,并且具有较高查询准确性。

一种分配查询的k-匿名位置隐私保护方案

针对基于位置服务中移动终端用户位置隐私保护存在时空关联和背景知识攻击的问题,提出一种分配查询的k-匿名位置隐私保护方案。基于查询概率和欧氏距离,在相邻网格区域内筛选假位置,合并真实用户位置形成k-匿名;通过兴趣点语义四叉树为假位置分配合理的查询,从时间、位置及查询3个维度提升用户被识别的不确定性,从而增强用户隐私度。安全性分析表明,提出的方案满足匿名性、不可伪造性和抵抗查询服务跟踪等安全特性。仿真实验表明,提出的方案具有较高的隐私保护度和效率以及较低的通信开销。

一种基于SHVE的连接查询动态对称可搜索加密方案

对称可搜索加密(searchable symmetric encryption,SSE)因其较高的搜索效率得到了人们的广泛关注.支持连接查询的对称可搜索加密方案可以提高方案的功能性.然而现有大部分连接查询方案不支持对加密数据库的动态更新操作,结合静态对称隐藏向量加密(symmetric hidden vectors encryption,SHVE)的定义,提出支持动态数据更新的动态对称隐藏向量加密(dynamic SHVE,DSHVE)的定义,并在此定义下构造了一种适用于可搜索加密应用场景的DSHVE方案.在此基础上,通过引入基于盲指数计算的不经意动态交叉标签,设计具有前向和后向隐私的向量数据存取结构,构造了支持连接查询的动态对称可搜索加密方案,给出了方案的详细工作过程和正式的安全性分析.理论分析和实验结果表明,连接查询时,该方案能够在不明显增加存储和计算开销的前提下,有效避免泄露匹配文档索引之外的结果特征.同时该方案能够仅通过单轮通信实现连接查询,具有较低的通信开销和较高的搜索效率.

基于平衡采样的主动半监督学习人类活动识别研究

基于可穿戴传感器的人类活动识别研究逐渐受到人们的广泛关注。本文提出了一种基于平衡采样的主动半监督模型,在挑选样本进行标注时,将样本的不确定性和多样性一并考虑在内,挑选出类别平衡的不确定性样本。确保训练后的模型对每个类都有很好的识别性能,从而提升整体分类结果。同时,为了全部利用标记和未标记样本的信息,将主动学习和半监督学习相结合,利用损失项信息不断更新网络参数,提升模型在低注释下的识别性能。该模型在2个公开数据集上得到了验证,在确保获得较优分类准确率的同时,可以大大减少样本的人工标注工作。

基于字典的压缩加密查询方案研究

RDF(资源描述框架,Resource Description Framework)压缩方案可以有效压缩庞大的RDF数据集,RDF加密方案可以有效提高数据的安全性。结合这两种方案,该文提出了一种基于字典的压缩加密查询方案。首先,通过原始的RDF数据集构建字典集,实现数据的压缩;然后,通过字典集生成密文ID三元组,实现数据的加密。将字典集存放在可信区域,密文ID三元组存储在不可信区域。查询时,首先通过字典集将查询语句中的关键词转换成对应的密文关键词,然后在密文ID三元组上查询与密文关键词相关的数据,将得到的密文数据传送至可信区域,在可信区域进行解密和解压缩操作,得到最终的明文数据。该方案将查询操作与解密解压缩操作分开,查询只在不可信区域操作,解密解压缩则在可信区域操作,从而进一步提高数据的安全性。最后,在5个数据集上与其他RDF加密方案进行了对比实验,实验结果证明了该压缩加密查询方案在查询性能上有一定的提升,验证了该方案是可行的和有效的。

基于人工智能技术的分布式数据库重复记录自动检测系统设计

以人工智能技术为基础前提的分布式数据库重复记录自动检测的方式,以提高数据库查询时的准确率以及查询效率。设计系统首先对数据信息进行对应的特征提取,而后通过权衡函数对样本信息进行整合,通过自适应分解得到相应的目标函数并求解,结合灰狼算法以及Shingle完成数据查询。经过算例验证,改进设计方式准确率均超过90%,平均耗时在35 s以内,满足自动查询快速精确的要求。

基于Sparse R-CNN的遥感目标检测研究

遥感图像目标检测任务在天气预报、环境监测及军事应用等领域均有应用,但其小目标众多、类间相似度大、尺度多样等问题导致提取特征困难。基于深度学习的方法在目标检测领域已经流行起来,Sparse R-CNN是一种结构简单且效果较好的模型,但将其直接应用到遥感图像上结果较差,针对遥感图像特点引入了自监督学习框架跟选择性查询收集提高了遥感图像目标检测的效果,在mAP指标上提高3.8个百分点。

遥感在自然资源确权登记中的应用研究

在自然资源确权登记过程中,自然资源调查是其中最基础的一项工作。传统的人工外业调查虽然精度高但是效率低、成本高、难以及时快速进行数据更新,满足不了我国大范围自然资源调查以及动态监测的需求。遥感技术以其独特的优势在自然资源确权登记工作中正被广泛应用。本文通过研究遥感技术在自然资源统一确权登记过程中的具体应用,深入探讨涉及的遥感关键技术和难点,并针对不足之处提出改进建议,保障遥感技术在自然资源确权登记领域更好地发挥作用。