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 issue...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.展开更多
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...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.展开更多
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...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.展开更多
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 ...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.展开更多
The rigid structure of the traditional relational database leads to data redundancy,which seriously affects the efficiency of the data query and cannot effectively manage massive data.To solve this problem,we use dist...The rigid structure of the traditional relational database leads to data redundancy,which seriously affects the efficiency of the data query and cannot effectively manage massive data.To solve this problem,we use distributed storage and parallel computing technology to query RDF data.In order to achieve efficient storage and retrieval of large-scale RDF data,we combine the respective advantage of the storage model of the relational database and the distributed query.To overcome the disadvantages of storing and querying RDF data,we design and implement a breadth-first path search algorithm based on the keyword query on a distributed platform.We conduct the LUBM query statements respectively with the selected data sets.In experiments,we compare query response time in different conditions to evaluate the feasibility and correctness of our approaches.The results show that the proposed scheme can reduce the storage cost and improve query efficiency.展开更多
The existing nearest neighbor query methods cannot directly perform the nearest neighbor query of specified geographical direction space.In order to compensate the shortcomings of the existing methods,a directional ne...The existing nearest neighbor query methods cannot directly perform the nearest neighbor query of specified geographical direction space.In order to compensate the shortcomings of the existing methods,a directional nearest neighbor query method in specific direction space based on Voronoi diagram is put forward.This work studies two cases,i.e.the query point is static and the query point moves with a constant velocity.Under the static condition,the corresponding pruning method and the pruning algorithm of the specified direction nearest neighbor(pruning_SDNN algorithm)are proposed by combining the plane right-angle coordinate system with the north-west direction,and then according to the smallest external rectangle of Voronoi polygon,the specific query is made and the direction nearest neighbor query based on Voronoi rectangle(VR-DNN) algorithm is given.In the case of moving with a constant velocity,first of all,the combination of plane right angle coordinate system,geographical direction and circle are used,the query range is determined and pruning methods and the pruning algorithm of the direction nearest neighbor based on decision circle(pruning_DDNN algorithm) are put forward.Then,according to the different position of motion trajectory and Voronoi diagram,a specific query through the nature of Voronoi diagram is given.At last,the direction nearest neighbor query based on Voronoi diagram and motion trajectory(VM-DNN) algorithm is put forward.The theoretical research and experiments show that the proposed algorithm can effectively deal with the problem of the nearest neighbor query for a specified geographical direction space.展开更多
文摘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.
文摘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.
文摘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.
基金Supported by the National Natural Science Foundation of China (No.61976098)the Natural Science Foundation for Outstanding Young Scholars of Fujian Province (No.2022J06023)。
文摘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.
基金This work is supported in part by National Natural Science Foundation of China(61728204)Innovation Funding(NJ20160028,NT2018027,NT2018028,NS2018057)+1 种基金Aeronautical Science Foundation of China(2016551500)State Key Laboratory for smart grid protection and operation control Foundation,Association of Chinese Graduate Education(ACGE).
文摘The rigid structure of the traditional relational database leads to data redundancy,which seriously affects the efficiency of the data query and cannot effectively manage massive data.To solve this problem,we use distributed storage and parallel computing technology to query RDF data.In order to achieve efficient storage and retrieval of large-scale RDF data,we combine the respective advantage of the storage model of the relational database and the distributed query.To overcome the disadvantages of storing and querying RDF data,we design and implement a breadth-first path search algorithm based on the keyword query on a distributed platform.We conduct the LUBM query statements respectively with the selected data sets.In experiments,we compare query response time in different conditions to evaluate the feasibility and correctness of our approaches.The results show that the proposed scheme can reduce the storage cost and improve query efficiency.
基金Supported by the National Natural Science Foundation of China(No.61872105,62072136)the Natural Science Foundation of Heilongjiang Province(No.LH2020F047)+1 种基金the Scientific Research Foundation for Returned Scholars Abroad of Heilongjiang Province of China(No.LC2018030)the National Key R&D Program of China(No.2020YFB1710200)。
文摘The existing nearest neighbor query methods cannot directly perform the nearest neighbor query of specified geographical direction space.In order to compensate the shortcomings of the existing methods,a directional nearest neighbor query method in specific direction space based on Voronoi diagram is put forward.This work studies two cases,i.e.the query point is static and the query point moves with a constant velocity.Under the static condition,the corresponding pruning method and the pruning algorithm of the specified direction nearest neighbor(pruning_SDNN algorithm)are proposed by combining the plane right-angle coordinate system with the north-west direction,and then according to the smallest external rectangle of Voronoi polygon,the specific query is made and the direction nearest neighbor query based on Voronoi rectangle(VR-DNN) algorithm is given.In the case of moving with a constant velocity,first of all,the combination of plane right angle coordinate system,geographical direction and circle are used,the query range is determined and pruning methods and the pruning algorithm of the direction nearest neighbor based on decision circle(pruning_DDNN algorithm) are put forward.Then,according to the different position of motion trajectory and Voronoi diagram,a specific query through the nature of Voronoi diagram is given.At last,the direction nearest neighbor query based on Voronoi diagram and motion trajectory(VM-DNN) algorithm is put forward.The theoretical research and experiments show that the proposed algorithm can effectively deal with the problem of the nearest neighbor query for a specified geographical direction space.