Maintenance Of Data Integrity Research Articles

Fortifying multi-cloud Kubernetes: Security strategies for the modern enterprise

Kubernetes-based multi-cloud architecture is becoming an integral part of modern enterprises, which makes ensuring its security a top priority. The main threats in such a system are related to configuration management, network security, access control and compliance with regulatory requirements. For effective security management, it is proposed to use CNAPP platforms that integrate several protection mechanisms, including cloud security Management (CSPM) and Kubernetes Security Management (KSPM). Automation of monitoring processes and authentication in Kubernetes, including centralized identity and access management, play an important role. The use of encryption technologies, software-defined networks (SDN) and regular audits contribute to reducing risks. The implementation of advanced security strategies and their constant adaptation to new threats ensures the sustainability of the cyber environment of multi-cloud deployments and the maintenance of data integrity.

Blockchain‐based data deduplication using novel content‐defined chunking algorithm in cloud environment

AbstractThe cloud environment is inherently dynamic as users are added immensely in a short duration. It is indeed difficult to manage such user profiles and associated data. Meanwhile, the cloud data expand at a twofold‐to‐threefold rate on average, making storage space management and data integrity maintenance a mandatory task but also risky. The main approaches for addressing these data maintenance challenges in a cloud context are deduplication and data protection. In order to manage storage space, finding and removing identical copies of the same data from the cloud are possible, resulting in a reduction in the amount of storage space needed. Furthermore, duplicate copies are considerably reduced in cloud storage owing to data deduplication. Here, a decentralized ledger public blockchain network is introduced to protect the Integrity of data stored in cloud storage. This research proposes data deduplication using speedy content‐defined Chunking (SpeedyCDC) algorithm in the public blockchain. Many people and businesses outsource sensitive data to remote cloud servers because it considerably eliminates the hassle of managing software and infrastructure. However, the ownership and control rights of users data are nonetheless divided because it is outsourced to cloud storage providers (CSPs) and kept on a distant cloud. As a result, users have a great deal of difficulty in verifying the Integrity of sensitive data. Analysis using datasets from Geospatial Information Systems (GIS) revealed that the throughput increased by 5%–6% over that of the fastCDC technique, which offered Integrity since a blockchain network secured it.

Estimating Uncertainties of Simulated MW Sounding Sensor Brightness Temperatures

Radiative transfer model (RTM) simulated microwave (MW) brightness temperatures (Tbs) are commonly used to monitor and evaluate space-based MW sensor-observed antenna temperature (Ta) data. Although these simulated Tbs are paramount to this data integrity maintenance activity, their uncertainties have not been quantified. This study develops and implements a method to estimate these simulated Tb uncertainties based on a statistical comparison of two Community RTM (CRTM)-simulated operational MW sounder Tb data sets, separately generated using the European Center for Medium Range Forecasts (ECMWF) Atmospheric Model High Resolution (HRES) and Global Navigation Satellite System (GNSS) Radio Occultation (RO) sounding inputs. The study shows the smallest single-sensor CRTM-simulated Tb uncertainties, computed from differences of ECMWF HRES and GNSS RO soundings-based simulated Tbs data sets, are on the order of 10−4 relative to a 300 K Tb for the two NOAA operational MW sounder channels with low- to mid-tropospheric peak weighting function sensitivity. Meanwhile, inter-sensor-simulated Tb differences, computed from the double difference of single-sensor-simulated Tb differences, lead to CRTM-simulated Tb uncertainties on the order for 10−4 for at least nine MW sounder channels with peak sensitivity from the low-troposphere to the low-stratosphere. These findings provide the basis of future work to assess the ability to identify and quantify suspected on-orbit MW sounder calibration anomalies using RTM-driven, on-orbit MW instrument monitoring techniques.

Streamlining PySpark Data Pipelines: Integrating External Data Sources and APIs

In the ever-expanding domain of big data analytics, integrating external data sources and APIs into data processing workflows is essential for enriching analyses and enhancing decision-making. PySpark, a prominent component of the Apache Spark ecosystem, offers robust capabilities for high-performance data processing but often requires extension to utilize external databases, web services, and third-party APIs effectively. This paper explores diverse methodologies for importing and managing data from these varied sources directly into PySpark. Techniques discussed include utilizing JDBC for database connections, employing HTTP clients for accessing RESTful APIs, and leveraging cloud storage APIs compatible with Hadoop. While integrating external data sources into PySpark pipelines significantly enriches the available data landscape, it also introduces challenges such as scalability of ingestion processes, maintenance of data integrity, and real-time data processing overhead. This study aims to provide a detailed examination of these integration techniques, highlighting both the strategic advantages and potential complications. By addressing these challenges, the paper presents a pathway towards more dynamic and comprehensive data analytics platforms, enabling businesses to leverage real-time insights and drive more nuanced analyses through PySpark.

Data Security of Dynamic and Robust Role Based Access Control from Multiple Authorities in Cloud Environment

Abstract: Data integrity maintenance is the major objective in cloud storage. It includes audition using TTP for unauthorized access. This work implements protecting the data and regeneration of data if someone mishandles it. This job will be assigned to a Proxy server. The data of the users will be stored in public and private area of the cloud. So that only public cloud data will be accessed by user and private cloud will remain more secured. Once any unauthorized modification is made, the original data in the private cloud will be retrieved by the Proxy server and will be returned to the user. Cloud storage generally provides different redundancy configuration to users in order to maintain the desired balance between performance and fault tolerance. Data availability is critical in distributed storage systems, especially when node failures are prevalent in real life. This research work explores secure data storage and sharing using proposed AES 128 encryption algorithm and Role Base Access Control (RBAC) for secure data access scheme for end user. This work also carried out backup server approach it works like proxy storage server for ad hoc data recovery for all distributed data servers. The experiment analysis has proposed in public as well as private cloud environment. Keywords: RBAC, Elgamal encryption scheme; secure user access policy; Proxy Key Generation, Role Base Access Control (RBAC), advanced encryption standard (AES), etc.

MDICA: Maintenance of data integrity in column-oriented database applications

Current information technologies generate large amounts of data for management or further analysis, storing it in NoSQL databases which provide horizontal scaling and high performance, supporting many read/write operations per second. NoSQL column-oriented databases, such as Cassandra and HBase, are usually modelled following a query-driven approach, resulting in denormalized databases where the same data can be repeated in several tables. Therefore, maintaining data integrity relies on client applications to ensure that, for data changes that occur, the affected tables will be appropriately updated. We devise a method called MDICA that, given a data insertion at a conceptual level, determines the required actions to maintain database integrity in column-oriented databases. This method is implemented for Cassandra database applications. MDICA is based on the definition of (1) rules to determine the tables that will be impacted by the insertion, (2) procedures to generate the statements to ensure data integrity and (3) messages to warn the user about errors or potential problems. This method helps developers in two ways: generating the statements needed to maintain data integrity and producing messages to avoid problems such as loss of information, redundant repeated data or gaps of information in tables.

A Retention-Aware Multilevel Cell Phase Change Memory Program Evaluation Metric

Multilevel cell (MLC) phase change memory (PCM) offers many potential advantages in scalability, bit-alterability, non-volatility, and high program speed. While many program approaches had been proposed, they were usually evaluated in energy, delay, or energy-delay product (EDP). These metrics, however, often overlook the data integrity maintenance (DIM) overhead caused by the resistance drift (R-drift) phenomenon in the MLC PCM. This letter proposes a program-maintenance (PM) metric, which is a modified EDP metric with DIM consideration. Furthermore, we evaluated the PM metric of various pulse shapes in iterative program–verify scheme. From the measured data of a 128M-cell MLC PCM chip, we showed that the PM metric of optimized pulse shape is better than those of the conventional SET and RESET pulse shapes up to 86.7%.

Public Encryption Techniques for Cloud Computing: Randomness and Performance Testing

Cloud computing becomes the next-generation architecture of IT Enterprises. Cloud computing is a technology. It enables clients to use high-end services in a form of software as a service. These reside on different servers all over the world. There are many security threats in cloud computing. Data security is one of them. Data security raises client concerns. There are many issues of data security. It named maintenance of data integrity, data hiding and data safety. These threats dominate clients concerns when the issue of cloud come up. Cloud computing has a big data. Traditional encryption method is time-consuming in this environment. Cloud computing has a single security architecture. It has also many customers with different demands of security. In this case, data security is considered one of the most important issues in cloud computing. The proposed work focused on accessing data securely in cloud and desktop environment. It depends on public key cryptosystem. Generally, Data security is an important factor for both cloud computing and traditional desktop applications. Client needs to have the highest possible level of privacy. Public key cryptosystem is a good candidate for this purpose. It plays a vital role in cloud computing security and desktop. The paper presents an evaluation for selected public key cryptosystem techniques. It named ElGamal cryptosystem, modified chaotic cryptosystem and chaotic cryptosystem. A modified chaotic cryptosystem was presented. The modification enabled the same message to have different cipher version. The experiments implemented at two independent platforms namely desktop computer and Amazon EC2 Micro Instance cloud computing environment. In this paper, the selected algorithms compared according to randomness testing. A standardized NIST statistical testing is used in both desktop environment and cloud computing environment. The algorithms are implemented using python charm Cryptography framework. Simulation results are shown to demonstrate the effectiveness of each algorithm. NIST statistical tests are used to determine suitable technique for cloud computing environment and desktop. It also used to study the performance of the selected encryption techniques in both environment. General Terms Cloud computing, Security. NIST statistical testing, chaotic, a Modified chaotic, ElGamal

Cloud Computing Security Framework based on Elliptical Curve

advancements in cloud computing due to increased connectivity and exponentially proliferating data has resulted in migration towards cloud architecture that employ dynamic load and access balancing. Cloud computing is technology where the can use high end services in form of software that reside on different servers and access data from all over the world. With a promising technology like this, it certainly abandons privacy, management of data and services, putting new security threats towards the assurance of data in cloud. However, there are some security concerns when clients handle and share data in the cloud- computing environment. The security threats such as maintenance of data integrity, data hiding and data safety dominate clients concerns when the issues of cloud security come up. The big data and time-consuming encryption calculations related to applying any encryption method have proved as a hindrance in this field. Cryptography is knowledge of protecting the information for providing encryption techniques In this paper, Cloud computing security framework was tested. The framework ensures a trusted cloud environment that controlled by both the client and the cloud environment. The proposed solution secures the movement of data between client and cloud end. the non-breakability of ElGamal based on Elliptic curve cryptography for data encryption/decryption was used along with Diffie Hellman based on elliptic curve mechanism for connection establishment, The data integrity verification is done by introducing a message digest tag for updating data based on SHA-256. The proposed encryption mechanism uses the combination of elliptical cryptography and SHA-256 methods.

Challenges Encountered During the Veterinary Disaster Response: An Example from Chile.

Simple SummaryDisaster preparedness for companion animals has economic, social and welfare benefits, yet many countries continue to omit dogs and cats from their national and regional contingency planning. Responses therefore, are often chaotic, inefficient and uncoordinated, or absent altogether. Documented experiences in Chile contribute to the information supporting the inclusion of companion animals into locally relevant disaster plans. These plans serve to prepare communities and authorities, identify resources available, establish a chain of command, develop local priorities, and subsequently reduce the negative impacts on both human and animal communities.Large-scale disasters have immeasurable effects on human and animal communities. Evaluating and reporting on the response successes and difficulties encountered serves to improve existing preparedness documents and provide support to those in the process of developing plans. Although the majority of disasters occur in low and middle income nations, less than 1% of the disaster literature originates from these countries. This report describes a response to a disease outbreak in domestic dogs in Dichato, Chile following the 2010 earthquake/tsunami. With no national plan coordinating the companion animal response, there was a chaotic approach among animal welfare organizations towards rescue, diagnosis, treatment and record-keeping. Similar to the medical response following the 1985 earthquake near Santiago, we experienced problems within our own teams in maintenance of data integrity and protocol compliance. Loss of infrastructure added complications with transportation, communications and acquisition of supplies. Similar challenges likely occur in most disasters, but can be reduced through pro-active planning at national and local levels. There is sufficient information to support the human and animal welfare benefits of including companion animals in national planning, and lessons learned through this and other experiences can assist planners in the development of comprehensive and locally relevant contingency plans.

DEVELOPMENT AND VALIDATION OF A NUCLEAR FUEL CYCLE ANALYSIS TOOL: A FUTURE CODE

This paper presents the development and validation methods of the FUTURE (FUel cycle analysis Tool for nUcleaR Energy) code, which was developed for a dynamic material flow evaluation and economic analysis of the nuclear fuel cycle. This code enables an evaluation of a nuclear material flow and its economy for diverse nuclear fuel cycles based on a predictable scenario. The most notable virtue of this FUTURE code, which was developed using C# and MICROSOFT SQL DBMS, is that a program user can design a nuclear fuel cycle process easily using a standard process on the canvas screen through a drag-and-drop method. From the user's point of view, this code is very easy to use thanks to its high flexibility. In addition, the new code also enables the maintenance of data integrity by constructing a database environment of the results of the nuclear fuel cycle analyses.

Improved verifiability scheme for data storage in cloud computing

In Cloud computing, data and service requests are responded by remote processes calls on huge data server clusters that are not totally trusted. The new computing pattern may cause many potential security threats. This paper explores how to ensure the integrity and correctness of data storage in cloud computing with user’s key pair. In this paper, we aim mainly at constructing of a quick data chunk verifying scheme to maintain data in data center by implementing a balance strategy of cloud computing costs, removing the heavy computing load of clients, and applying an automatic data integrity maintenance method. In our scheme, third party auditor (TPA) is kept in the scheme, for the sake of the client, to periodically check the integrity of data blocks stored in data center. Our scheme supports quick public data integrity verification and chunk redundancy strategy. Compared with the existing scheme, it takes the advantage of ocean data support and high performance.

Mobile Java RMI support over heterogeneous wireless networks: A case study

Distributed object-oriented platforms are increasingly important in wireless environments to provide frameworks for collaborative computation and for managing a large pool of distributed resources. One of the important layers for implementing distributed computing in such environments is via remoting mechanisms. For example, Java uses Remote Method Invocation (RMI) for handling distributed controls. In this paper, we investigate the support for this important layer on wireless environments and address the issues to support Java RMI over heterogeneous wireless environments. We present a case study for supporting Java RMI in Bluetooth, GPRS, and WLAN environments, which represents an important middleware for component communications. The Bluetooth layer is supported by incorporating a set of protocol stack layers for Bluetooth, known as JavaBT that has been developed by us, and by supporting an L2CAP layer with sockets to support Java RMI sockets. RMI over GPRS/WLAN is achieved by RMI implementation over IP layer. Our support for the roaming of Java RMI over heterogeneous wireless networks is based on the concept of direct connection, which avoids the problems caused by forwarding. The difficulty of this strategy is how to handle the existing connection when the mobile node moves to another location so as to avoid interruption of the high-level applications. We solve this problem in Java RMI by the support of dynamic addresses and dynamic sockets. We also propose algorithms to handle the handoff process. In addition, methods for connect-loss detection and data-integrity maintenance in dealing with roaming scenarios are also presented. Java Grande benchmarks are used to demonstrate that our RMI implementations over GPRS, WLAN, and Bluetooth networks are effective in supporting parallel and distributed control of Java layers in heterogeneous wireless environments.

ThermoData Engine (TDE): Software Implementation of the Dynamic Data Evaluation Concept. 2. Equations of State on Demand and Dynamic Updates over the Web

ThermoData Engine (TDE) is the first full-scale software implementation of the dynamic data evaluation concept, as reported recently in this journal. The present paper describes two major software enhancements to TDE: (1) generation of equation of state (EOS) representations on demand and (2) establishment of a dynamically updated experimental data resource for use in the critical evaluation process. Four EOS formulations have been implemented in TDE for on-demand evaluation: the volume translated Peng-Robinson, modified Sanchez-Lacombe, PC-SAFT, and Span Wagner EOS. The equations are fully described with their general application. The class structure of the program is described with particular emphasis on special features required to implement an equation, such as an EOS, that represents multiple properties simultaneously. Full implementation of the dynamic data evaluation concept requires that evaluations be based on an up-to-date "body of knowledge" or, in the case of TDE, an up-to-date collection of experimental results. A method to provide updates through the World Wide Web is described that meets the challenges of maintenance of data integrity with full traceability. Directions for future enhancements are outlined.

Implementation of a continuous quality improvement program for percutaneous coronary intervention and cardiac surgery at a large community hospital

Continuous quality improvement (CQI) is widely used in other industries and has been promoted as a method for quality control in medicine. The national databases developed by the American College of Cardiology and the Society of Thoracic Surgeons have greatly facilitated data collection for CQI. Hospitals can encounter barriers to CQI, however, which include creating the proper organizational infrastructure and engaging physicians and hospital administrators in the process. These barriers are particularly evident in large community hospitals. We describe the organizational infrastructure for CQI, including committee structure, methods of repeated data collection and feedback, and maintenance of data integrity and confidentiality. We report demographic data and clinical outcomes for patients undergoing percutaneous coronary intervention and coronary artery bypass surgery before and after implementation of our CQI program. Since 1995, we have maintained a CQI process driven by repeated collection of valid, confidential, operator-specific data. We have observed sustained physician and administration participation and buy-in. During the follow-up period, patient complexity increased and observed outcomes improved, although the improvement was clearly multifactorial. We describe the organization of a CQI program at a large complex community hospital. Our CQI program was successfully implemented, has been sustained, and is associated in observed improvement in patient outcomes. The program described here may be a useful model for other similar hospitals that are attempting to create a program to address quality improvement.

Maintaining data integrity in insect biodiversity assessment projects

The success of projects involving assessment of insect biodiversity depends on many things, but one which is often overlooked is the maintenance of data integrity. This is an issue best considered from project conception, through the design phase to the completion of the sample, specimen and data processing phase. This paper considers some guiding principles and details some logical steps that will help avoid loss of data integrity.

Resource integration using a large knowledge base in Carnot

A method for integrating separately developed information resources that overcomes incompatibilities in syntax and semantics and permits the resources to be accessed and modified coherently is described. The method provides logical connectivity among the information resources via a semantic service layer that automates the maintenance of data integrity and provides an approximation of global data integration across systems. This layer is a fundamental part of the Carnot architecture, which provides tools for interoperability across global enterprises. >

Methodology for allocating resources for data quality enhancement

The issue of effective use of resources committed to data integrity maintenance is addressed via a heuristic which uses parameter estimates that are iteratively refined to partition available resources among multiple data sets.

Distributed database development

Centralized databases have been in large-scale use in the data processing industry since the early 1970s. Distributed databases are very much more sophisticated than centralized databases, and they involve complex software to handle problems such as concurrency control, deadlock prevention and the maintenance of data integrity during multi-site updates. It is only recently that these software problems have been understood, and products which incorporate this software have only very recently started to appear on the market. The question of what constitutes a distributed database is the subject of some debate, and in order to clarify the issue the paper works systematically through a set of twelve rules to which a distributed database should ideally conform. The current state of the art for each rule is summarized briefly. In spite of the contentious subject of what actually constitutes a distributed database, the area of distributed databases is one in which there is considerable interest, and it promises to have a considerable growth rate in the future.