A New Encryption Mechanism Supporting the Update of Encrypted Data for Secure and Efficient Collaboration in the Cloud Environment

With the rise of remote collaboration,the demand for advanced storage and collaboration tools has rapidly increased.However,traditional collaboration tools primarily rely on access control,leaving data stored on cloud servers vulnerable due to insufficient encryption.This paper introduces a novel mechanism that encrypts data in‘bundle’units,designed to meet the dual requirements of efficiency and security for frequently updated collaborative data.Each bundle includes updated information,allowing only the updated portions to be reencrypted when changes occur.The encryption method proposed in this paper addresses the inefficiencies of traditional encryption modes,such as Cipher Block Chaining(CBC)and Counter(CTR),which require decrypting and re-encrypting the entire dataset whenever updates occur.The proposed method leverages update-specific information embedded within data bundles and metadata that maps the relationship between these bundles and the plaintext data.By utilizing this information,the method accurately identifies the modified portions and applies algorithms to selectively re-encrypt only those sections.This approach significantly enhances the efficiency of data updates while maintaining high performance,particularly in large-scale data environments.To validate this approach,we conducted experiments measuring execution time as both the size of the modified data and the total dataset size varied.Results show that the proposed method significantly outperforms CBC and CTR modes in execution speed,with greater performance gains as data size increases.Additionally,our security evaluation confirms that this method provides robust protection against both passive and active attacks.

Research on Tensor Multi-Clustering Distributed Incremental Updating Method for Big Data

The scale and complexity of big data are growing continuously,posing severe challenges to traditional data processing methods,especially in the field of clustering analysis.To address this issue,this paper introduces a new method named Big Data Tensor Multi-Cluster Distributed Incremental Update(BDTMCDIncreUpdate),which combines distributed computing,storage technology,and incremental update techniques to provide an efficient and effective means for clustering analysis.Firstly,the original dataset is divided into multiple subblocks,and distributed computing resources are utilized to process the sub-blocks in parallel,enhancing efficiency.Then,initial clustering is performed on each sub-block using tensor-based multi-clustering techniques to obtain preliminary results.When new data arrives,incremental update technology is employed to update the core tensor and factor matrix,ensuring that the clustering model can adapt to changes in data.Finally,by combining the updated core tensor and factor matrix with historical computational results,refined clustering results are obtained,achieving real-time adaptation to dynamic data.Through experimental simulation on the Aminer dataset,the BDTMCDIncreUpdate method has demonstrated outstanding performance in terms of accuracy(ACC)and normalized mutual information(NMI)metrics,achieving an accuracy rate of 90%and an NMI score of 0.85,which outperforms existing methods such as TClusInitUpdate and TKLClusUpdate in most scenarios.Therefore,the BDTMCDIncreUpdate method offers an innovative solution to the field of big data analysis,integrating distributed computing,incremental updates,and tensor-based multi-clustering techniques.It not only improves the efficiency and scalability in processing large-scale high-dimensional datasets but also has been validated for its effectiveness and accuracy through experiments.This method shows great potential in real-world applications where dynamic data growth is common,and it is of significant importance for advancing the development of data analysis technology.

A Spatio-temporal Data Model for Road Network in Data Center Based on Incremental Updating in Vehicle Navigation System

The technique of incremental updating,which can better guarantee the real-time situation of navigational map,is the developing orientation of navigational road network updating.The data center of vehicle navigation system is in charge of storing incremental data,and the spatio-temporal data model for storing incremental data does affect the efficiency of the response of the data center to the requirements of incremental data from the vehicle terminal.According to the analysis on the shortcomings of several typical spatio-temporal data models used in the data center and based on the base map with overlay model,the reverse map with overlay model (RMOM) was put forward for the data center to make rapid response to incremental data request.RMOM supports the data center to store not only the current complete road network data,but also the overlays of incremental data from the time when each road network changed to the current moment.Moreover,the storage mechanism and index structure of the incremental data were designed,and the implementation algorithm of RMOM was developed.Taking navigational road network in Guangzhou City as an example,the simulation test was conducted to validate the efficiency of RMOM.Results show that the navigation database in the data center can response to the requirements of incremental data by only one query with RMOM,and costs less time.Compared with the base map with overlay model,the data center does not need to temporarily overlay incremental data with RMOM,so time-consuming of response is significantly reduced.RMOM greatly improves the efficiency of response and provides strong support for the real-time situation of navigational road network.

Maintaining Consistency Based on Timely Updating Records List in Workflow Data

In order to settle the problem of workflow data consis-tency under the distributed environment, an invalidation strategy based-on timely updating record list is put forward. The strategy adopting the method of updating the records list and the recovery mechanism of updating message proves the classical invalidation strategy. When the request cycle of duplication is too long, the strategy uses the method of updating the records list to pause for sending updating message; when the long cycle duplication is requested again, it uses the recovery mechanism to resume the updating message. This strategy not only ensures the consistency of the workflow data, but also reduces the unnecessary network traffic. From theoretical comparison with those common strategies, the unnecessary network traffic of this strategy is fewer and more stable. The simulation results validate this conclusion.

Fingerprint Database Updating Using Crowdsourcing in Indoor Bluetooth Positioning System

Fingerprint⁃based Bluetooth positioning is a popular indoor positioning technology.However,the change of indoor environment and Bluetooth anchor locations has significant impact on signal distribution,which will result in the decline of positioning accuracy.The widespread extension of Bluetooth positioning is limited by the need of manual effort to collect the fingerprints with position labels for fingerprint database construction and updating.To address this problem,this paper presents an adaptive fingerprint database updating approach.First,the crowdsourced data including the Bluetooth Received Signal Strength(RSS)sequences and the speed and heading of the pedestrian were recorded.Second,the recorded crowdsourced data were fused by the Kalman Filtering(KF),and then fed into the trajectory validity analysis model with the purpose of assigning the unlabeled RSS data with position labels to generate candidate fingerprints.Third,after enough candidate fingerprints were obtained at each Reference Point(RP),the Density⁃based Spatial Clustering of Applications with Noise(DBSCAN)approach was conducted on both the original and the candidate fingerprints to filter out the fingerprints which had been identified as the noise,and then the mean of fingerprints in the cluster with the largest data volume was selected as the updated fingerprint of the corresponding RP.Finally,the extensive experimental results show that with the increase of the number of candidate fingerprints and update iterations,the fingerprint⁃based Bluetooth positioning accuracy can be effectively improved.

Use of Incremental Analysis Updates in 4D-Var Data Assimilation

The four-dimensional variational （4D-Var） data assimilation systems used in most operational and research centers use initial condition increments as control variables and adjust initial increments to find optimal analysis solutions. This approach may sometimes create discontinuities in analysis fields and produce undesirable spin ups and spin downs. This study explores using incremental analysis updates （IAU） in 4D-Var to reduce the analysis discontinuities. IAU-based 4D-Var has almost the same mathematical formula as conventional 4D-Var if the initial condition increments are replaced with time-integrated increments as control variables. The IAU technique was implemented in the NASA/GSFC 4D-Var prototype and compared against a control run without IAU. The results showed that the initial precipitation spikes were removed and that other discontinuities were also reduced, especially for the analysis of surface temperature.

Health Data Availability Protection:Delta-XOR-Relay Data Update in Erasure-Coded Cloud Storage Systems

To achieve the high availability of health data in erasure-coded cloud storage systems,the data update performance in erasure coding should be continuously optimized.However,the data update performance is often bottlenecked by the constrained cross-rack bandwidth.Various techniques have been proposed in the literature to improve network bandwidth efficiency,including delta transmission,relay,and batch update.These techniques were largely proposed individually previously,and in this work,we seek to use them jointly.To mitigate the cross-rack update traffic,we propose DXR-DU which builds on four valuable techniques:(i)delta transmission,(ii)XOR-based data update,(iii)relay,and(iv)batch update.Meanwhile,we offer two selective update approaches:1)data-deltabased update,and 2)parity-delta-based update.The proposed DXR-DU is evaluated via trace-driven local testbed experiments.Comprehensive experiments show that DXR-DU can significantly improve data update throughput while mitigating the cross-rack update traffic.

Transient content caching and updating with modified harmony search for Internet of Things

Internet of Things (IoT) has emerged as one of the new use cases in the 5th Generation wireless networks. However, the transient nature of the data generated in IoT networks brings great challenges for content caching. In this paper, we study a joint content caching and updating strategy in IoT networks, taking both the energy consumption of the sensors and the freshness loss of the contents into account. In particular, we decide whether or not to cache the transient data and, if so, how often the servers should update their contents. We formulate this content caching and updating problem as a mixed 0–1 integer non-convex optimization programming, and devise a Harmony Search based content Caching and Updating (HSCU) algorithm, which is self-learning and derivativefree and hence stipulates no requirement on the relationship between the objective and variables. Finally, extensive simulation results verify the effectiveness of our proposed algorithm in terms of the achieved satisfaction ratio for content delivery, normalized energy consumption, and overall network utility, by comparing it with some baseline algorithms.

Probabilistic data association algorithm based on ensemble Kalman filter with observation iterated update

Aiming at improving the observation uncertainty caused by limited accuracy of sensors,and the uncertainty of observation source in clutters,through the dynamic combination of ensemble Kalman filter(EnKF) and probabilistic data association(PDA),a novel probabilistic data association algorithm based on ensemble Kalman filter with observation iterated update is proposed.Firstly,combining with the advantages of data assimilation handling observation uncertainty in EnKF,an observation iterated update strategy is used to realize optimization of EnKF in structure.And the object is to further improve state estimation precision of nonlinear system.Secondly,the above algorithm is introduced to the framework of PDA,and the object is to increase reliability and stability of candidate echo acknowledgement.In addition,in order to decrease computation complexity in the combination of improved EnKF and PDA,the maximum observation iterated update mechanism is applied to the iteration of PDA.Finally,simulation results verify the feasibility and effectiveness of the proposed algorithm by a typical target tracking scene in clutters.

Research and Application of Synchronization Update of Distributed Heterogeneous Database

According to the different equipment, different system and heterogeneous database have be information ＂isolated island＂ problem, and the data of equipments can be updated in real time on the business node. The paper proposes a program of data synchronization platform based on J2EE （JMS） and XML, and detailed analysis and description of the workflow system, its frame structure and the key technology. Practice shows that this scheme has the advantages of convenient and real-time etc..

基于Data Window的数据管理与更新机制的研究与应用

介绍了DataWindow控件管理数据的基本原理和处理流程 ,并深入阐述了DataWindow对象的四个缓冲区 (PrimaryBuffer,DeleteBuffer,FilterBuffer,OriginalBuffer)的功能和相互关系 ,以及它们之间是如何各司其职 ,协作完成数据的增加、删除、修改 ,并把结果提交给数据库管理系统的基本原理和过程。最后讨论了DataWindow对象的数据更新机制在编程中的一些应用 。

基于TQuery与TUpdateQuery的多表缓冲更新技术

以C++Builder中的TQuery和TUpdatequery控件为例,阐述了进行现场数据的查询和缓冲更新的原理和技术,并解决了更新查询中的多个基表的问题。同时介绍了TDataSource控件的动态数据交换与验证的功能。最后在计算机上编制程序实现了以数据缓冲技术实现多表更新的问题。

基于线性特征边界线的实景三维动态更新关键技术研究

实景三维中国建设是落实数字中国、平安中国、数字经济战略的重要举措,是新时期国家重要新型基础设施,是服务生态文明建设和经济社会发展的基础支撑。“十四五”以来,各地已经陆续开展了一定规模的实景三维建设项目,积累了不同尺度的实景三维底板数据。如何充分利用已有建设成果,且持续保障实景三维数据的时效性、可用性,已经成为下一个亟需解决的问题。文章针对实景三维数据的场景特征属性,提出一种线性特征边界线的实景三维动态更新和场景融合的方案。基于原有场景数据,将目标更新区域最邻近线性特征边界线作为实际更新范围,对其进行切割、拼接、调色、融合等处理,实现多期实景三维数据的无缝融合。

PB中数据窗口的Update Properties研究

介绍了PB中数据窗口的Update Properties各项的含义和作用,利用这些属性可以设置数据窗口关联的数据库表是否能被更新、并发控制及键的更新方式,使得用户不仅懂得设置还知道其所以然。

关于数据库更新UPDATE命令的算法

本文从教学方法的角度讨论用其它工作区中数据库更新当前工作区中数据库的更新命令 UPDATE的算法，并通过实例加以说明 .

一种特殊DATA CUBE的技术研究

封闭数据立方体利用元组间的关联,除去冗余信息,在减小数据立方体体积的同时,避免了查询时的解压缩。从源数据的分组角度对封闭数据立方体概念进行了解释,并在此基础上详细分析了由于源数据的更新而导致的对应封闭数据立方体的更新过程,给出了更新算法的框架。

Mode of Operation for Modification, Insertion, and Deletion of Encrypted Data

Due to the development of 5G communication,many aspects of information technology(IT)services are changing.With the development of communication technologies such as 5G,it has become possible to provide IT services that were difficult to provide in the past.One of the services made possible through this change is cloud-based collaboration.In order to support secure collaboration over cloud,encryption technology to securely manage dynamic data is essential.However,since the existing encryption technology is not suitable for encryption of dynamic data,a new technology that can provide encryption for dynamic data is required for secure cloudbased collaboration.In this paper,we propose a new encryption technology to support secure collaboration for dynamic data in the cloud.Specifically,we propose an encryption operation mode which can support data updates such as modification,addition,and deletion of encrypted data in an encrypted state.To support the dynamic update of encrypted data,we invent a new mode of operation technique named linked-block cipher(LBC).Basic idea of our work is to use an updatable random value so-called link to link two encrypted blocks.Due to the use of updatable random link values,we can modify,insert,and delete an encrypted data without decrypt it.

基于多源数据融合的水利工程测量信息动态更新系统

现有的水利工程测量信息动态更新系统,在传输测量信息时没有最佳的路径概念,采用车轮搜索,导致信息更新所需时间较长,为优化系统运行效率,设计基于多元数据融合设计水利工程测量信息动态更新系统。设计可以增强数据更新速度的芯片作为系统硬件;构造有效路径代价函数,整编水利工程测量多源数据;选择数据传输最短路径;建立最小二乘估计数学模型,引入多元数据融合设计信息更新算法。实验结果显示:在系统更新时间的测试中,面对不同数量的并行服务器,三个对照组用时均高于实验组,可见该系统具备更好的运行效率。

基于MEC边缘云的智慧商城数据更新控制算法

针对智慧商城中数据量较大的问题,提出基于MEC边缘云的智慧商城数据更新控制算法。在MEC边缘云环境中将数据信息经过整合分类处理后直接推送至用户端,将万有引力搜索算法和布谷鸟搜索算法融合构建CS-GSA算法,用于MEC边缘云智慧商城数据库数据清洗。在差分数据更新方法中引入游程编码和哈夫曼编码,采用游程编码将连续重复的数据压缩处理,降低数据规模,然后通过哈夫曼编码再次压缩,降低传输过程中的通信开销,实现MEC边缘云智慧商城数据更新。实验结果表明,所提方法的解编码时间、数据更新时间更短、通信开销较小,说明其能够提高数据更新质量。

网络异常检测领域概念漂移问题研究综述

【目的】随着网络技术的快速发展和广泛应用,网络异常检测作为保护网络安全和维护系统正常运行的手段变得越来越重要。然而,网络中异常行为和攻击手段不断变化,给异常检测带来了新的挑战。其中,概念漂移问题是网络异常检测领域中受到广泛关注的难点之一。【方法】本综述旨在对网络异常检测领域中概念漂移问题进行研究分析和总结。与前人的研究相比,本文将专注于网络异常检测领域的流数据。【文献范围】首先,对概念漂移进行详细介绍,包括定义、产生原因和特点。通过对概念漂移的全面理解,可以为后续的检测方法提供指导。其次,系统性地介绍了概念漂移检测方法,主要包括基于统计的方法、机器学习方法和深度学习方法等,并对比了各类方法的优缺点和适用场景。最后,探讨了概念漂移在未来可能的研究方向。【结论】本文聚焦于网络异常检测领域的概念漂移问题,通过详细介绍概念漂移的定义、产生原因和特点,以及深入分析和总结针对流数据概念漂移的检测方法,为未来研究方向提供了有价值的参考和指导。