Cloud Data Storage Research Articles

Analysis and Evaluation of Intel Software Guard Extension-Based Trusted Execution Environment Usage in Edge Intelligence and Internet of Things Scenarios

With the extensive deployment and application of the Internet of Things (IoT), 5G and 6G technologies and edge intelligence, the volume of data generated by IoT and the number of intelligence applications derived from these data are rapidly growing. However, the absence of effective mechanisms to safeguard the vast data generated by IoT, along with the security and privacy of edge intelligence applications, hinders their further development and adoption. In recent years, Trusted Execution Environment (TEE) has emerged as a promising technology for securing cloud data storage and cloud processing, demonstrating significant potential for ensuring data and application confidentiality in more scenarios. Nevertheless, applying TEE technology to enhance security in IoT and edge intelligence scenarios still presents several challenges. This paper investigates the technical challenges faced by current TEE solutions, such as performance overhead and I/O security issues, in the context of the resource constraints and data mobility that are inherent to IoT and edge intelligence applications. Using Intel Software Guard Extensions (SGX) technology as a case study, this paper validates these challenges through extensive experiments. The results provide critical assessments and analyses essential for advancing the development and usage of TEE in IoT and edge intelligence scenarios.

A privacy preserving batch audit scheme for IoT based cloud data storage

A privacy preserving batch audit scheme for IoT based cloud data storage

Certificateless Public Integrity Checking of Group Shared Data in Cloud Storage

Cloud storage service supplies people with an efficient method to share data within a group. The cloud server is not trustworthy, so lots of remote data possession checking (RDPC) protocols are proposed and thought to be an effective way to ensure the data integrity. However, most of RDPC protocols are based on the mechanism of traditional public key infrastructure (PKI), which has obvious security flaw and bears big burden of certificate management. To avoid this shortcoming, identity-based cryptography (IBC) is often chosen to be the basis of RDPC. Unfortunately, IBC has an inherent drawback of key escrow. To solve these problems, we utilize the technique of certificateless signature to present a new RDPC protocol for checking the integrity of data shared among a group. In our scheme, user's private key includes two parts: a partial key generated by the group manager and a secret value chosen by herself/himself. To ensure the right public keys are chosen during the data integrity checking, the public key of each user is associated with her unique identity, for example the name or telephone number. Thus, the certificate is not needed and the problem of key escrow is eliminated too. Meanwhile, the data integrity can still be audited by public verifier without downloading the whole data. In addition, our scheme also supports efficient user revocation from the group. The security of our scheme is reduced to the assumptions of computational Diffie- Hellman (CDH) and discrete logarithm (DL). Experiment results exhibit that the new protocol is very efficient and feasible

A UTILIZAÇÃO DA TECNOLOGIA DA INFORMAÇÃO COMO FERRAMENTA DE APOIO NA GESTÃO CONTÁBIL

This work aims to examine how information technology (IT) can be used as a strategic tool to support and improve accounting management. With technological advancement and the growing need for efficiency in business activities, IT has become fundamental in automating repetitive tasks, optimizing data accuracy and integrating accounting systems. The study investigates the application of technological tools, such as management systems, advanced data analysis and cloud storage, to ensure efficiency in decision-making, information security and regulatory compliance. Through a literature review, this work highlights the benefits of technology in reducing failures and expenses, as well as facilitating the production of real-time reports and ensuring compliance. The data indicates that IT not only transforms the way accounting is carried out, but also increases the competitiveness of organizations by enabling faster, more reliable and efficient accounting management. In summary, this study provides a structured view of the role of information technology in modernizing accounting and improving financial management processes.

An optimized and efficient multiuser data sharing using the selection scheme design secure approach and federated learning in cloud environment

An optimized and efficient multiuser data sharing using the selection scheme design secure approach and federated learning in cloud environment

Data Recovery in Cloud Data Storage

Data Recovery in Cloud Data Storage

FUZZY MODEL OF ELECTRICITY CONTROL WITH WIRELESS INFORMATION PROCESSED ON GPU

Methods of processing information transmitted through wireless networks with software development are investigated in the work. Innovative methods of data transmission such as optical technologies, quantum data transmission and wireless data transmission technologies are disclosed. It is noted that in the modern understanding, the concept of distributed computing defines the process of convergence (convergence) of distributed processing methods, such as GRID, cloud and fog computing, with the combination of virtual cluster systems (grid clusters, cloud clusters and fog clusters) into a single information communication and computing system . It is emphasized that, unlike cellular modems, ZigBee technology nodes have microcontrollers with a pre-installed operating system and flash memory, which allows solving simple computational tasks in real time before sending data. It is advisable to solve such tasks within the framework of a multi-agent approach, which will increase the efficiency of the use of sensor nodes and the entire sensor network. The advantages of the multi-agent technology of fog computing based on sensor nodes of the wireless network of the ZigBee standard are revealed. The method of multi-agent processing of sensory information and its main components are described. The architecture of the system of distributed sensor data processing is outlined, which includes 4 hardware and software levels: Terminal sensor nodes and controllers of measuring devices and automation devices that implement fuzzy calculations; Coordinators, sensor segment routers and cellular modems that collect, protect and transmit sensor data to the processing center; A data processing center that includes a cluster of servers for GRID calculations and a cloud data storage server; Client devices to access cloud storage, computing cluster servers, and distributed fog computing terminals. It is emphasized that indicators and forecast results can be stored on distributed sensor nodes or transmitted for accumulation in cloud storage for further extraction and intelligent processing in the GRID cluster of the data center.

Enhancing healthcare with AI: Sustainable AI and IoT-Powered ecosystem for patient aid and interpretability analysis using SHAP

The healthcare industry has blossomed into one of the most pivotal and technologically advanced sectors in the past decade. Individuals grapple with the peril of untimely demise from diverse ailments as patients suffer from delayed treatment. The paramount objective is to forge a dependable patient care system utilizing the Internet of Things (IoT), enabling physicians to monitor patients' well-being within medical facilities or even the confines of their homes. The system aids in tracking the patient's SpO2 level, body temperature, pulse rate (beats per minute), room temperature, and humidity, then trains the data with machine learning algorithms for the patient and finally monitors it through the Blynk IoT system. The cloud-stored data can be harnessed to ascertain and supervise one's health and predict forthcoming perils. This study unveils an efficacious decision-making model custom-tailored for Internet of Things (IoT) ventures, and the proposed trained algorithm satiates these requirements, offering efficiency and precision, rendering it appropriate for numerous IoT applications. Finally, the Shapley Additive Explanations (SHAP) is used here for finding out the most influential parameters, and Explainable AI (XAI) is utilized with the help of SHAP values for enhanced information on affecting parameters. The SVC model's hyperparameter is properly adjusted, yielding a testing accuracy of 98.83 % and a training accuracy of 98.71 %. On cross-validation, the lightweight Sklearn model achieved a mean accuracy of almost 99 %. And with a SHAP weightage magnitude of 1.38, 0.91, and 0.44 for Class ‘Good’, Class ‘Poor’, and Class ‘Bad’, respectively, patients SpO2 level is the most significant feature.

Enhancement of the Security of Data by Associating Obfuscation Technique Along with Steganography

The safety of stored data in the cloud is of the utmost significance. There is a lot of room for user data in cloud storage. This technology has proven to be a groundbreaking effort because of its ability to offer cheap, extensive computational storage that users may access from anywhere at any time. By entrusting their data to the cloud, users may take advantage of services that are flexible, efficient, and run smoothly. Once data has been transferred to the cloud, the onus for its administration shifts entirely to the CSP. Data stored in the cloud raises a number of security problems, in addition to the benefits. Data in transit and data at rest are both susceptible to assaults when users move their data to the cloud. Data protection and building confidence in cloud services are now top priorities. Data should be kept in an encrypted or masked format to prevent unauthorised access. One major problem with cloud computing is the lack of assurance around data protection. By integrating obfuscation with steganography, this study reveals a novel and advanced method to strengthen data security. To guarantee confidentiality, the proposed method incorporates obfuscation and steganography techniques. In contrast to steganography, whose main goal is to mask the existence of information, obfuscation primarily seeks to transform data into a different form while simultaneously concealing the original data. Using the Least Significant Bit (LSB) replacement technique, the hidden text may be hidden inside a picture. The results of the experiments show that the proposed method may successfully integrate large amounts of data and generate high-quality stego images.

Certificateless integrity auditing scheme for sensitive information protection in cloud storage

Data integrity auditing provides a method for checking the integrity of outsourced data in cloud storage. However, outsourced data often contain sensitive information (such as names), posing risks of exposure during data sharing. To address this issue, Ming et al. proposed a certificateless integrity auditing scheme for sensitive information protection, claiming its security. However, by demonstrating two specific attack scenarios, we pointed out its security vulnerabilities. Subsequently, we proposed a new certificateless integrity auditing scheme for sensitive information protection in cloud storage (CIAS-SIP), which supports sensitive information protection and does not specify the data blocks that need sanitization by the data owner (DO). In addition, it supports dynamic operations by the DO on outsourced data (insertion, deletion, and modification) and provides security proofs based on the discrete logarithm problem. Finally, we compared CIAS-SIP’s performance with three other integrity auditing schemes for sensitive information protection. The results show that CIAS-SIP exhibits superior efficiency.

Advancements in Securing Cloud-Stored Data and Managing Sensitive Information: A Comprehensive Review of Ciphertext-Policy Attribute-Based Encryption (CP-ABE) Techniques

Abstract—The exploring comprehensive review of cutting-edge techniques for securing cloud-stored data and managing sensitive information in the context of smart cities through the application of Ciphertext-Policy Attribute-Based Encryption (CP-ABE). It highlights the innovative integration of blockchain technology with CP-ABE, which introduces a decentralized and tamper- resistant key management system, thereby enhancing the overall security framework in cloud environments where data sharing is prevalent. Introducing an online/offline multi-authority CP- ABE scheme, characterized by hidden policies, offers significant advancements in protecting user attributes and access structures, ensuring that sensitive information remains confidential even during encryption and decryption processes. This dual approach not only fortifies security but also optimizes the efficiency of data- sharing mechanisms. Furthermore, the paper delves into imple- menting hidden sensitive policies and keyword search techniques within smart city infrastructures, which are designed to facilitate secure and efficient data retrieval. These techniques ensure that while data remains accessible to authorized users, privacy is rigorously maintained. Collectively, these approaches represent significant strides in bolstering the security and confidentiality of data in both cloud-based and smart city applications, addressing the growing demand for robust and efficient data management solutions in increasingly interconnected environments. Index Terms—Ciphertext-Policy Attribute-Based Encryption (CP-ABE), Blockchain, Key Management, Decentralized Secu- rity, Cloud-Stored Data, Yolo V7, Explainable AI (XAI), On- line/Offline Multi-Authority Scheme, Hidden policies, Privacy Preservation, Data Protection, Secure Data Management

Clinical Prediction using Machine Learning-based IoT for E-Healthcare Systems

Machine learning is one of the fundamental approaches to nature the data produced by Internet of Things (IoT) and has drastic enhancements to enhance the decision-making fields such as education, security, business, and especially the healthcare sector. This integration of ML with IoT makes it possible for healthcare systems to generate patterns from big data which can improve the efficiency of predictive analytics and referrals. It also helps in the development of templates for medical records and real-time patient care, and the identification of patient diagnosis electronically. Though, performance of the ML algorithms can be affected by the dataset on the basis of which clinical decisions are made. Such variations clearly demonstrate the requirement to gain a deeper understanding of how several ML approaches work in terms of IoT data analysis for healthcare. In the proposed study we are testing the Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithms for tracking health parameters. We first employ the above-mentioned approach for training and validation of the ML models on the database available at UCI. In the testing phase, the IoT devices record actual data of heart rate, blood pressure, posture, and temperature to forecast a health anomaly. Subsequently, an examination of accuracy of the aforementioned prediction is performed on the cloud stored data with reference to those produced by K-nearest neighbour (KNN) algorithm. Such an approach makes it possible to give adequate consideration to the offered assessment of the role of ML in increasing the efficiency of IoT-based healthcare systems with regard to early detection and comprehensive intervention practices. In this way, this research help to consider variability in prognostic outcomes for more accurate and precise clinical decision-making processes, and thus contribute in enhancing patient care and health in IoT environment.

Simulation of Segmented Clustering of Cloud Storage Data Based on Neural Network Models and Python

Simulation of Segmented Clustering of Cloud Storage Data Based on Neural Network Models and Python

Cybersecurity Implications of Edge Computing in Data Engineering

Edge computing, as an extension of cloud computing, brings computation and data storage closer to the data source. This shift offers significant advantages in terms of latency reduction, bandwidth optimization, and real-time processing capabilities. By minimizing the distance that data needs to travel, edge computing enhances the performance of applications that require rapid data processing and immediate response times. This is particularly beneficial for Internet of Things (IoT) devices, autonomous vehicles, smart grids, and other applications that demand low-latency interactions. However, the decentralization inherent in edge computing introduces a unique set of cybersecurity challenges that are distinct from those faced in traditional centralized cloud environments. The distributed architecture of edge computing creates numerous points of vulnerability, each of which can be exploited by cyber attackers. Additionally, edge devices often operate with limited computational resources and power, which complicates the implementation of robust security measures. This paper explores the cybersecurity implications of edge computing in the context of data engineering. It begins with a comprehensive review of existing literature to establish the current understanding of edge computing security issues. The review identifies the primary vulnerabilities associated with edge computing, including those related to distributed architecture, data transmission, and resource constraints. Following the literature review, the paper delves into specific security threats that edge computing environments face. These include man-in-the-middle (MitM) attacks, distributed denial-of-service (DDoS) attacks, malware and ransomware, and insider threats. Each threat is analyzed to understand its potential impact on edge computing systems and the data they process.

Integrating NoSQL, Hilbert Curve, and R*-Tree to Efficiently Manage Mobile LiDAR Point Cloud Data

The widespread use of Light Detection and Ranging (LiDAR) technology has led to a surge in three-dimensional point cloud data; although, it also poses challenges in terms of data storage and indexing. Efficient storage and management of LiDAR data are prerequisites for data processing and analysis for various LiDAR-based scientific applications. Traditional relational database management systems and centralized file storage struggle to meet the storage, scaling, and specific query requirements of massive point cloud data. However, NoSQL databases, known for their scalability, speed, and cost-effectiveness, provide a viable solution. In this study, a 3D point cloud indexing strategy for mobile LiDAR point cloud data that integrates Hilbert curves, R*-trees, and B+-trees was proposed to support MongoDB-based point cloud storage and querying from the following aspects: (1) partitioning the point cloud using an adaptive space partitioning strategy to improve the I/O efficiency and ensure data locality; (2) encoding partitions using Hilbert curves to construct global indices; (3) constructing local indexes (R*-trees) for each point cloud partition so that MongoDB can natively support indexing of point cloud data; and (4) a MongoDB-oriented storage structure design based on a hierarchical indexing structure. We evaluated the efficacy of chunked point cloud data storage with MongoDB for spatial querying and found that the proposed storage strategy provides higher data encoding, index construction and retrieval speeds, and more scalable storage structures to support efficient point cloud spatial query processing compared to many mainstream point cloud indexing strategies and database systems.

Boolean Searchable Attribute-Based Signcryption With Search Results Self-Verifiability Mechanism for Data Storage and Retrieval in Clouds

Boolean Searchable Attribute-Based Signcryption With Search Results Self-Verifiability Mechanism for Data Storage and Retrieval in Clouds

Modern approaches to data storage: comparison of relational and cloud data warehouses using etl and elt methods

The paper analyses various aspects of the use of relational and cloud data warehouses as well as methods of integrating ETL and ELT data. A comparative analysis of these approaches, their advantages and disadvantages are provided. A central relational data warehouse is proposed that provides a single version of truth (SVOT), which allows standardising and structuring data, avoiding differences and providing the access to the same information for all users of an organisation. It is analysed the methodological approaches to implementing a data warehouse: top-down, bottom-up, and from middle. It is described cloud data warehouses that use cloud technologies to provide scalability, availability and fault tolerance, which is important for the companies with huge amounts of data. The advantages and disadvantages of ETL and ELT are analysed: ETL transforms data before it is loaded into the warehouse, which makes it easier to maintain data confidentiality. ELT performs transformation after loading, which allows for more flexible data processing directly in the warehouse. In the article, we deal with the approaches to implementing a data warehouse: top-down is suitable for strategic planning, bottom-up allows for faster results, and the middle approach combines both methods to achieve optimal efficiency. We considered cloud data storage: compared to relational storage, cloud storage is more flexible, scalable and efficient, providing speed and reducing infrastructure costs. It is described cloud storage architectures: massive parallel processing, hybrid architectures, lambda architectures, and multi-structured architectures. They provide high performance and flexibility in data processing. It is described data storage technologies: Data Lake, Polyglot Persistence, Apache Iceberg, Apache Parquet, and columnar databases that provide efficient storage and processing of large amounts of data

An intelligent optimized secure blockchain mechanism for cloud auditing

The cloud auditing model was attractive for the cloud storage system to check the integrity of the stored user files. Hence, to maximize the security concern, federated learning and the blockchain were introduced. But, in some cases, security issues arose because of the extensive unstructured data and attack vulnerability. So, to avoid security problems and offer better protection in a cloud environment, a novel Optimized Buffalo-based Homomorphic SHA Blockchain (OBHSB) is proposed. In this model for accessing the cloud storage data with the critical matching method, if any of the unauthenticated users are trying to access the file initially, the system checks the key matching parameter. The proposed model was developed to provide better security in big data presented in the cloud environment. Hence, federated learning was adopted to create the cloud auditing phase in the distributed cloud environment. Therefore, it gives flexibility in training all data types: audio, text, and images. Moreover, the prime performance parameters were calculated and compared with other traditional approaches to justify the efficiency of the proposed model. The attack was considered an event in this research. The performance rate of the proposed model was validated in the performance analysis. Subsequently, the case study was developed in this research to explain the working procedure of the proposed design, and the result of the proposed model was discussed.

Revolutionizing Cloud Security: Innovative Approaches for Safeguarding Data Integrity

Cloud computing uses third-party servers or private clouds to provide online services and on-demand enterprise resource access. Microsoft Azure, Amazon, IBM, and Google Apps allow customers to design and run cloud-based apps from anywhere. These cloud services store and retrieve data from faraway servers, highlighting the importance of security due to internet data transfer. Before using cloud computing, firms must handle security issues. Pay-per-use, self-service, and scalability make the model popular in banking, healthcare, retail, education, manufacturing, and business. Pay-per-use models let users use servers, networks, storage, services, and applications without buying them. Limited data control can result in account service issues, traffic hijacking, insecure APIs, malicious insiders, technology sharing issues, and multi-tenancy data failures. Continuous research and development improve security and user confidence. This paper provides a framework for understanding cloud computing, identifies security dangers and research issues, and emphasizes its relevance in major industries. It also recommends cloud security innovations and assesses worldwide data protection policies to improve data security and reduce risks. Cloud-stored data is vulnerable to programs with security weaknesses, notwithstanding its benefits. If the guest OS’s security is unreliable, virtualization on a hypervisor may expose data to attacks. Additionally, data security vulnerabilities in transit and at rest will be addressed.

Enabling Parity Authenticator-Based Public Auditing With Protection of a Valid User Revocation in Cloud

The significance of the cloud enables the data owners (DOs) to store data remotely in cloud server (CS). The external and internal attacks on the stored data at CS can deliberately remove data. Furthermore, the CS removes the stored data to make empty location for the user's upcoming new data. However, it is a legal expectation of DOs to know whether their data are correctly stored or altered in CS. In this article, we propose a novel privacy-aware and hash-parity-bits-based public auditing (PA-HPPA) framework to secure full data, left half of the data, and the right half of the data, generated by a DO. DO generates two private key pairs with the assistance of a virtual key and a user ID (IP). The virtual key is the sequence number of DO who is registered and provided by the trusted data manager (TDM) while IP is the sequence number of DO working in an organization. Subsequently, DO blinds the categorized data and generates their signatures and hashes. In addition, DO generates the parity bits using xor and assigns to each hard drive (HD) in CS, which assistants to TDM in public auditing. Second, how to identify the error in the stored data and how to securely recover the error/missed data? Extension to the framework, the novel proposed data error identification and secure data recovery produce tags for installed HDs of CS using truth table and recover the altered/missed data via a authenticator, which is produced using xor function. Third, how to protect a valid user from revocation and, in case a user has revoked on merit basis, then how to securely access the stored data of it? This novel work has proposed three conditions to meet the validity of the valid user from revocation and securely generating the public-private key pairs to access the stored data of the revoked user securely from CS. Fourth, there is an efficient novel proposed dynamic operation scheme to insert, update, or delete the stored data at CS without regenerating the signatures, hashes, and tags for the whole stored data in cloud. The security analysis and the performance evaluation of the proposed solutions are provably efficient and secure with reduced communication costs.