Data Component:An Innovative Framework for Information Value Metrics in the Digital Economy

The increasing dependence on data highlights the need for a detailed understanding of its behavior,encompassing the challenges involved in processing and evaluating it.However,current research lacks a comprehensive structure for measuring the worth of data elements,hindering effective navigation of the changing digital environment.This paper aims to fill this research gap by introducing the innovative concept of“data components.”It proposes a graphtheoretic representation model that presents a clear mathematical definition and demonstrates the superiority of data components over traditional processing methods.Additionally,the paper introduces an information measurement model that provides a way to calculate the information entropy of data components and establish their increased informational value.The paper also assesses the value of information,suggesting a pricing mechanism based on its significance.In conclusion,this paper establishes a robust framework for understanding and quantifying the value of implicit information in data,laying the groundwork for future research and practical applications.

Optimizing data aggregation and clustering in Internet of things networks using principal component analysis and Q-learning

The Internet of things(IoT)is a wireless network designed to perform specific tasks and plays a crucial role in various fields such as environmental monitoring,surveillance,and healthcare.To address the limitations imposed by inadequate resources,energy,and network scalability,this type of network relies heavily on data aggregation and clustering algorithms.Although various conventional studies have aimed to enhance the lifespan of a network through robust systems,they do not always provide optimal efficiency for real-time applications.This paper presents an approach based on state-of-the-art machine-learning methods.In this study,we employed a novel approach that combines an extended version of principal component analysis(PCA)and a reinforcement learning algorithm to achieve efficient clustering and data reduction.The primary objectives of this study are to enhance the service life of a network,reduce energy usage,and improve data aggregation efficiency.We evaluated the proposed methodology using data collected from sensors deployed in agricultural fields for crop monitoring.Our proposed approach(PQL)was compared to previous studies that utilized adaptive Q-learning(AQL)and regional energy-aware clustering(REAC).Our study outperformed in terms of both network longevity and energy consumption and established a fault-tolerant network.

A Data Security Framework for Cloud Computing Services

Cyberattacks are difficult to prevent because the targeted companies and organizations are often relying on new and fundamentally insecure cloudbased technologies,such as the Internet of Things.With increasing industry adoption and migration of traditional computing services to the cloud,one of the main challenges in cybersecurity is to provide mechanisms to secure these technologies.This work proposes a Data Security Framework for cloud computing services(CCS)that evaluates and improves CCS data security from a software engineering perspective by evaluating the levels of security within the cloud computing paradigm using engineering methods and techniques applied to CCS.This framework is developed by means of a methodology based on a heuristic theory that incorporates knowledge generated by existing works as well as the experience of their implementation.The paper presents the design details of the framework,which consists of three stages:identification of data security requirements,management of data security risks and evaluation of data security performance in CCS.

Fortifying Healthcare Data Security in the Cloud:A Comprehensive Examination of the EPM-KEA Encryption Protocol

A new era of data access and management has begun with the use of cloud computing in the healthcare industry.Despite the efficiency and scalability that the cloud provides, the security of private patient data is still a majorconcern. Encryption, network security, and adherence to data protection laws are key to ensuring the confidentialityand integrity of healthcare data in the cloud. The computational overhead of encryption technologies could leadto delays in data access and processing rates. To address these challenges, we introduced the Enhanced ParallelMulti-Key Encryption Algorithm (EPM-KEA), aiming to bolster healthcare data security and facilitate the securestorage of critical patient records in the cloud. The data was gathered from two categories Authorization forHospital Admission (AIH) and Authorization for High Complexity Operations.We use Z-score normalization forpreprocessing. The primary goal of implementing encryption techniques is to secure and store massive amountsof data on the cloud. It is feasible that cloud storage alternatives for protecting healthcare data will become morewidely available if security issues can be successfully fixed. As a result of our analysis using specific parametersincluding Execution time (42%), Encryption time (45%), Decryption time (40%), Security level (97%), and Energyconsumption (53%), the system demonstrated favorable performance when compared to the traditional method.This suggests that by addressing these security concerns, there is the potential for broader accessibility to cloudstorage solutions for safeguarding healthcare data.

Clustering algorithm for multiple data streams based on spectral component similarity

A new algorithm for clustering multiple data streams is proposed.The algorithm can effectively cluster data streams which show similar behavior with some unknown time delays.The algorithm uses the autoregressive （AR） modeling technique to measure correlations between data streams.It exploits estimated frequencies spectra to extract the essential features of streams.Each stream is represented as the sum of spectral components and the correlation is measured component-wise.Each spectral component is described by four parameters,namely,amplitude,phase,damping rate and frequency.The ε-lag-correlation between two spectral components is calculated.The algorithm uses such information as similarity measures in clustering data streams.Based on a sliding window model,the algorithm can continuously report the most recent clustering results and adjust the number of clusters.Experiments on real and synthetic streams show that the proposed clustering method has a higher speed and clustering quality than other similar methods.

Evaluation on Land Ecological Security in Hohhot Based on Principal Component Analysis

Based on the status of land ecological resources in Hohhot, 20 indexes covering nature, resource environment, economy and society were selected and the evaluation index system was established. With the principal component analysis, the land ecological security of Hohhot from 2009 to 2015 was analyzed. The results showed that the land ecological security of Hohhot was declining year by year in 2009-2015. Besides, per capital GDP and public green area, the proportion of in- dustry and the price index of agricultural and animal husbandry production materials were the key factors influencing the land ecological security of Hohhot. The key for protection of the land ecological security may lie in the protection of land quality and prevention of land degradation in farming and stock-breeding areas.

Classification of Big Data Security Based on Ontology Web Language

A vast amount of data (known as big data) may now be collected and stored from a variety of data sources, including event logs, the internet, smartphones, databases, sensors, cloud computing, and Internet of Things (IoT) devices. The term “big data security” refers to all the safeguards and instruments used to protect both the data and analytics processes against intrusions, theft, and other hostile actions that could endanger or adversely influence them. Beyond being a high-value and desirable target, protecting Big Data has particular difficulties. Big Data security does not fundamentally differ from conventional data security. Big Data security issues are caused by extraneous distinctions rather than fundamental ones. This study meticulously outlines the numerous security difficulties Large Data analytics now faces and encourages additional joint research for reducing both big data security challenges utilizing Ontology Web Language (OWL). Although we focus on the Security Challenges of Big Data in this essay, we will also briefly cover the broader Challenges of Big Data. The proposed classification of Big Data security based on ontology web language resulting from the protégé software has 32 classes and 45 subclasses.

NONLINEAR DATA RECONCILIATION METHOD BASED ON KERNEL PRINCIPAL COMPONENT ANALYSIS

In the industrial process situation, principal component analysis (PCA) is ageneral method in data reconciliation. However, PCA sometime is unfeasible to nonlinear featureanalysis and limited in application to nonlinear industrial process. Kernel PCA (KPCA) is extensionof PCA and can be used for nonlinear feature analysis. A nonlinear data reconciliation method basedon KPCA is proposed. The basic idea of this method is that firstly original data are mapped to highdimensional feature space by nonlinear function, and PCA is implemented in the feature space. Thennonlinear feature analysis is implemented and data are reconstructed by using the kernel. The datareconciliation method based on KPCA is applied to ternary distillation column. Simulation resultsshow that this method can filter the noise in measurements of nonlinear process and reconciliateddata can represent the true information of nonlinear process.

Data Security Storage Model of the Internet of Things Based on Blockchain

With the development of information technology,the Internet of Things(IoT)has gradually become the third wave of the worldwide information industry revolution after the computer and the Internet.The application of the IoT has brought great convenience to people’s production and life.However,the potential information security problems in various IoT applications are gradually exposed and people pay more attention to them.The traditional centralized data storage and management model of the IoT is easy to cause transmission delay,single point of failure,privacy disclosure and other problems,and eventually leads to unpredictable behavior of the system.Blockchain technology can effectively improve the operation and data security status of the IoT.Referring to the storage model of the Fabric blockchain project,this paper designs a data security storage model suitable for the IoT system.The simulation results show that the model is not only effective and extensible,but also can better protect the data security of the Internet of Things.

Data security governance in the era of big data: status, challenges, and prospects

At present,the global big data industry is experiencing vibrant development with technological evolution and application innovation speeding up side by side.Novel technologies for data storage,computing and analytics,such as non-relational databases,distributed parallel computing,machine learning,and deep data mining have emerged and evolved rapidly.Big data has gradually become a fundamental national strategic resource and a cornerstone factor in production.However,increasing big data security problems are exposed and the number of big data security incidents occurring around the world is on the rise.Against this background,this paper provides a summary of the present situation of global data security governance,points out the challenges,and then proceeds to raise solutions for further modernizing data security governance systems.

Identification and classification of transient pulses observed in magnetometer array data by time-domain principal component analysis filtering

A method for identification of pulsations in time series of magnetic field data which are simultaneously present in multiple channels of data at one or more sensor locations is described. Candidate pulsations of interest are first identified in geomagnetic time series by inspection. Time series of these ＂training events＂ are represented in matrix form and transpose-multiplied to generate time- domain covariance matrices. The ranked eigenvectors of this matrix are stored as a feature of the pulsation. In the second stage of the algorithm, a sliding window （approxi- mately the width of the training event） is moved across the vector-valued time-series comprising the channels on which the training event was observed. At each window position, the data covariance matrix and associated eigen- vectors are calculated. We compare the orientation of the dominant eigenvectors of the training data to those from the windowed data and flag windows where the dominant eigenvectors directions are similar. This was successful in automatically identifying pulses which share polarization and appear to be from the same source process. We apply the method to a case study of continuously sampled （50 Hz） data from six observatories, each equipped with three- component induction coil magnetometers. We examine a 90-day interval of data associated with a cluster of four observatories located within 50 km of Napa, California, together with two remote reference stations-one 100 km to the north of the cluster and the other 350 km south. When the training data contains signals present in the remote reference observatories, we are reliably able to identify and extract global geomagnetic signals such as solar-generated noise. When training data contains pulsations only observed in the cluster of local observatories, we identify several types of non-plane wave signals having similar polarization.

Protecting Data Mobility in Cloud Networks Using Metadata Security

At present,health care applications,government services,and banking applications use big data with cloud storage to process and implement data.Data mobility in cloud environments uses protection protocols and algorithms to secure sensitive user data.Sometimes,data may have highly sensitive information,lead-ing users to consider using big data and cloud processing regardless of whether they are secured are not.Threats to sensitive data in cloud systems produce high risks,and existing security methods do not provide enough security to sensitive user data in cloud and big data environments.At present,several security solu-tions support cloud systems.Some of them include Hadoop Distributed File Sys-tem(HDFS)baseline Kerberos security,socket layer-based HDFS security,and hybrid security systems,which have time complexity in providing security inter-actions.Thus,mobile data security algorithms are necessary in cloud environ-ments to avoid time risks in providing security.In our study,we propose a data mobility and security(DMoS)algorithm to provide security of data mobility in cloud environments.By analyzing metadata,data are classified as secured and open data based on their importance.Secured data are sensitive user data,whereas open data are open to the public.On the basis of data classification,secured data are applied to the DMoS algorithm to achieve high security in HDFS.The pro-posed approach is compared with the time complexity of three existing algo-rithms,and results are evaluated.

Data Security and Privacy in Cloud Storage

In this paper, we survey data security and privacy problems created by cloud storage applications and propose a cloud storage security architecture. We discuss state-of-the-art techniques for ensuring the privacy and security of data stored in the cloud. We discuss policies for access control and data integrity, availability, and privacy. We also discuss several key solutions proposed in current literature and point out future research directions.

Towards Developing Privacy-Preserved Data Security Approach(PP-DSA)in Cloud Computing Environment

In the present scenario of rapid growth in cloud computing models,several companies and users started to share their data on cloud servers.However,when the model is not completely trusted,the data owners face several security-related problems,such as user privacy breaches,data disclosure,data corruption,and so on,during the process of data outsourcing.For addressing and handling the security-related issues on Cloud,several models were proposed.With that concern,this paper develops a Privacy-Preserved Data Security Approach(PP-DSA)to provide the data security and data integrity for the out-sourcing data in Cloud Environment.Privacy preservation is ensured in this work with the Efficient Authentication Technique(EAT)using the Group Signature method that is applied with Third-Party Auditor(TPA).The role of the auditor is to secure the data and guarantee shared data integrity.Additionally,the Cloud Service Provider(CSP)and Data User(DU)can also be the attackers that are to be handled with the EAT.Here,the major objective of the work is to enhance cloud security and thereby,increase Quality of Service(QoS).The results are evaluated based on the model effectiveness,security,and reliability and show that the proposed model provides better results than existing works.

Security-Critical Components Recognition Algorithm for Complex Heterogeneous Information Systems

With the skyrocketing development of technologies,there are many issues in information security quantitative evaluation(ISQE)of complex heterogeneous information systems(CHISs).The development of CHIS calls for an ISQE model based on security-critical components to improve the efficiency of system security evaluation urgently.In this paper,we summarize the implication of critical components in different filed and propose a recognition algorithm of security-critical components based on threat attack tree to support the ISQE process.The evaluation model establishes a framework for ISQE of CHISs that are updated iteratively.Firstly,with the support of asset identification and topology data,we sort the security importance of each asset based on the threat attack tree and obtain the security-critical components(set)of the CHIS.Then,we build the evaluation indicator tree of the evaluation target and propose an ISQE algorithm based on the coefficient of variation to calculate the security quality value of the CHIS.Moreover,we present a novel indicator measurement uncertainty aiming to better supervise the performance of the proposed model.Simulation results show the advantages of the proposed algorithm in the evaluation of CHISs.

Data Mining Based on Principal Component Analysis Application to the Nitric Oxide Response in Escherichia coli

This work evaluates a recently developed multivariate statistical method based on the creation of pseudo or latent variables using principal component analysis （PCA）. The application is the data mining of gene expression data to find a small subset of the most important genes in a set of thousand or tens of thousands of genes from a relatively small number of experimental runs. The method was previously developed and evaluated on artificially generated data and real data sets. Its evaluations consisted of its ability to rank the genes against known truth in simulated data studies and to identify known important genes in real data studies. The purpose of the work described here is to identify a ranked set of genes in an experimental study and then for a few of the most highly ranked unverified genes, experimentally verify their importance.This method was evaluated using the transcriptional response of Escherichia coli to treatment with four distinct inhibitory compounds： nitric oxide, S-nitrosoglutathione, serine hydroxamate and potassium cyanide. Our analysis identified genes previously recognized in the response to these compounds and also identified new genes.Three of these new genes, ycbR, yJhA and yahN, were found to significantly （p-values〈0.002） affect the sensitivityofE, coli to nitric oxide-mediated growth inhibition. Given that the three genes were not highly ranked in the selected ranked set （RS）, these results support strong sensitivity in the ability of the method to successfully identify genes related to challenge by NO and GSNO. This ability to identify genes related to the response to an inhibitory compound is important for engineering tolerance to inhibitory metabolic products, such as biofuels, and utilization of cheap sugar streams, such as biomass-derived sugars or hydrolysate.

Block Level Data Integrity Assurance Using Matrix Dialing Method towards High Performance Data Security on Cloud Storage

Data outsourcing through cloud storage enables the users to share on-demand resources with cost effective IT services but several security issues arise like confidentiality, integrity and authentication. Each of them plays an important role in the successful achievement of the other. In cloud computing data integrity assurance is one of the major challenges because the user has no control over the security mechanism to protect the data. Data integrity insures that data received are the same as data stored. It is a result of data security but data integrity refers to validity and accuracy of data rather than protect the data. Data security refers to protection of data against unauthorized access, modification or corruption and it is necessary to ensure data integrity. This paper proposed a new approach using Matrix Dialing Method in block level to enhance the performance of both data integrity and data security without using Third Party Auditor (TPA). In this approach, the data are partitioned into number of blocks and each block converted into a square matrix. Determinant factor of each matrix is generated dynamically to ensure data integrity. This model also implements a combination of AES algorithm and SHA-1 algorithm for digital signature generation. Data coloring on digital signature is applied to ensure data security with better performance. The performance analysis using cloud simulator shows that the proposed scheme is highly efficient and secure as it overcomes the limitations of previous approaches of data security using encryption and decryption algorithms and data integrity assurance using TPA due to server computation time and accuracy.

Big Data Security and Privacy： A Review

While Big Data gradually become a hot topic of research and business and has been everywhere used in many industries, Big Data security and privacy has been increasingly concerned. However, there is an obvious contradiction between Big Data security and privacy and the widespread use of Big Data. In this paper, we firstly reviewed the enormous benefits and challenges of security and privacy in Big Data. Then, we present some possible methods and techniques to ensure Big Data security and privacy.

Data Security Issues and Challenges in Cloud Computing: A Conceptual Analysis and Review

Cloud computing is a set of Information Technology services offered to users over the web on a rented base. Such services enable the organizations to scale-up or scale-down their in-house foundations. Generally, cloud services are provided by a third-party supplier who possesses the arrangement. Cloud computing has many advantages such as flexibility, efficiency, scalability, integration, and capital reduction. Moreover, it provides an advanced virtual space for organizations to deploy their applications or run their operations. With disregard to the possible benefits of cloud computing services, the organizations are reluctant to invest in cloud computing mainly due to security concerns. Security is one of the main challenges that hinder the growth of cloud computing. At the same time, service providers strive to reduce the risks over the clouds and increase their reliability in order to build mutual trust between them and the cloud customers. Various security issues and challenges are discussed in this research, and possible opportunities are stated.

An Effective Long String Searching Algorithm towards Component Security Testing

In the execution of method invocation sequences to test component security,abnormal or normal information is generated and recorded in a monitor log. By searching abnormal information from monitor log,the exceptions that the component has can be determined. To facilitate the searching process,string searching methods could be employed. However,current approaches are not effective enough to search long pattern string. In order to mine the specific information with less number of matches,we proposed an improved Sunday string searching algorithm in this paper. Unlike Sunday algorithm which does not make use of the already matched characters,the proposed approach presents two ideas — utilizing and recycling these characters. We take advantage of all matched characters in main string,if they are still in the matchable interval compared with pattern string,to increase the distance that pattern string moves backwards. Experimental analysis shows that,compared to Sunday algorithm,our method could greatly reduce the matching times,if the scale of character set constituting both main string and pattern string is small,or if the length of pattern string is long. Also,the proposed approach can improve the search effectiveness for abnormal information in component security testing.