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.

A Database Querying Language for Formulating Relational Queries on Small Devices

For small devices like the PDAs and mobile phones, formulation of relational database queries is not as simple as using conventional devices such as the personal computers and laptops. Due to the restricted size and resources of these smaller devices, current works mostly limit the queries that can be posed by users by having them predetermined by the developers. This limits the capability of these devices in supporting robust queries. Hence, this paper proposes a universal relation based database querying language which is targeted for small devices. The language allows formulation of relational database queries that uses minimal query terms. The formulation of the language and its structure will be described and usability test results will be presented to support the effectiveness of the language.

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.

GPU数据库实现技术发展演进

爆炸式增长的数据对存储和处理数据提出了更高的需求,GPU数据库作为新硬件数据库的一个重要分支,在大容量和高性能处理方面有其独特的优势.GPU数据库作为高性能数据库的代表,在最近几年受到学术界和产业界的关注,一批具有代表性的研究成果和标志性的实际产品已经出现.GPU数据库的技术发展按照GPU加速型和GPU内存型两种技术路线展开.两种技术路线都有相应的原型系统或产品出现.虽然两种GPU数据库的发展路线在实现上有所不同,但GPU数据库最基本的功能部分和核心技术是相似的,都有查询编译、查询优化、查询执行以及存储管理等功能.当前主流的数据传输方案除了PCIe之外,NVLink、RDMA和CXL等传输方案也为不同处理器之间的数据传输提供了更多的可能性.大多数GPU数据库使用列存储模型来存储数据,少数GPU数据库(如PG-Strom)对两种存储模型都支持.在列存储模型上利用压缩技术能减少数据的存储空间和传输时延.在GPU数据库上进行的压缩和解压的时间应该在整个数据处理的过程中占比很少.在GPU数据库上建立和维护索引不应该有很大的系统开销.JIT编译时间短、编译效率高,是GPU数据库编译的主流.操作符对数据库查询性能的影响非常明显,连接操作、分组聚集和OLAP运算符是目前研究最多的三个类型.目前大多数的研究中,连接和分组聚集算子通常结合在一起研究.在连接算子执行的过程中还和表的连接顺序结合在一起进行考虑.OLAP算子是GPU数据库中的又一个被大量研究的算子,GPU数据库在OLAP算子和模型方面持续受到研究者的关注.GPU数据库有三种查询处理模型,即行处理、列处理和向量化处理.向量化处理和列处理在实际系统中应用较多.由于GPU加速型数据库技术的发展,CPU-GPU协同处理模型上的查询方案与查询引擎也有一定数量的研究成果出现.当前GPU数据库的查询优化研究主要有三部分:多表连接顺序、查询重写和代价模型.然而,GPU数据库的代价评估模型在目前还没有很好的解决方案,GPU数据库的查询优化在未来仍有很大的研究空间.事务在GPU数据库中没有得到很好的研究,尽管有单独的原型系统,但目前的研究还没有取得重大进展.本文总结了GPU数据库各种关键技术已有的研究成果,指出GPU数据库当前存在的问题和面临的挑战,对未来的研究方向进行了展望.

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。

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。

一种车载端为主的城市路网当前与未来速度查询方法

城市智能交通管理中经常查询路段的当前和未来交通速度,该文提出一种车载边缘为主(VED)的城市路段速度查询和预测方法:车载端在速度低于一定阈值时,与其它车载端交换收集到的数据,并在本地构建轻量级的当前和历史速度索引,以支持当前速度查询。为了用尽可能少的模型支持速度预测,提出根据路段拓扑同构将路网划分成若干路段等价类,根据周期性时窗和路段等价类将整个时空划分成若干模型等价类,同一个模型等价类的路段在给定时窗呈现相似的交通运行模式。针对每个模型等价类,车载端和数据中心配合进行联邦学习,训练长短期记忆模型(LSTMs)并存储在车载端,以响应车载端对附近未来交通状况的查询。每个车载端本地索引数据、本地响应查询,避免了查询响应延迟和通信拥塞;数据保存在车载端,而非集中存放,避免了安全攻击导致的隐私泄漏。

面向分布式数据库的算子并行优化策略

随着网络技术的不断发展,数据规模呈现爆发式增长,使得传统的单机数据库逐步被分布式数据库所取代。分布式数据库采用节点协同工作方式解决了大规模数据存储问题,但由于增加了节点间通信开销,查询效率却不如单机数据库。分布式架构下,存储节点的数据仅用作多备份的冗余,为系统故障时提供数据恢复,并未被利用起来改善查询效率。针对上述问题,提出了一种面向分布式数据库的算子并行优化策略,通过对关键物理算子进行拆分,将拆分后的子请求均匀分配到存储层多个节点,由多个节点并行处理,从而减少查询响应时间。上述策略已经在分布式数据库CBase上进行了应用,实验表明,提出的并行优化策略可显著缩短SQL请求查询时间,并提高系统资源利用率。

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。

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%。

基于差分隐私的路网环境skyline查询

路网中的skyline查询在智慧交通、兴趣点发现和位置服务等领域具有重要的应用价值,但存在查询效率较低、未考虑查询结果的隐私性等问题。有鉴于此,文中提出了一种基于差分隐私的路网环境下skyline查询方法。首先,针对路网环境下的初始数据集数据量大和数据复杂的特点,对数据集进行预处理,利用基于距离属性划分的skyline层和路网Voronoi图的性质提出了3个剪枝规则,基于剪枝规则给出了路网环境下的数据集剪枝算法,从而有效地过滤掉大量冗余数据;其次,针对过滤后的数据集,利用网格索引的存储方式来节省存储空间,并设计了基于网格索引的skyline扩展树,基于扩展树和相应的剪枝规则提出了查询全局候选skyline点集的算法;最后,针对查询结果集,利用差分隐私预算分配模型来分配隐私预算,并基于信息散度进行结果集发布,有效提高了数据信息的隐私性。实验结果表明:所提出的查询方法的准确率在99%以上;其在数据集规模较大情况下的查询效率相较于传统skyline查询方法提升10%以上;在总差分隐私预算为0.01、0.10、0.50和1.00时,所提出的隐私预算分配方法的相对误差均低于等差分配和等比分配方法。

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。