Cloud Data Storage Research Articles

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

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.

Cloud storage auditing and data sharing with data deduplication and private information protection for cloud-based EMR

In the cloud-based electronic medical records (EMRs) system, the doctors upload EMRs to the cloud for storage and sharing. However, the integrity of EMRs stored in the cloud cannot be guaranteed and sharing EMRs may leak the patients’ private information. Furthermore, shared EMRs contain a lot of duplicate data, leading to data redundancy. To solve the above issues, we propose a cloud storage auditing and data sharing scheme with data deduplication and private information protection for cloud-based EMR. In our proposed scheme, we introduce the sanitizer to sanitize the data blocks containing the private information in EMR, and transform these blocks’ authenticators into the authenticators of sanitized EMR. The authenticators are utilized to check the data integrity. Using the above method, the EMR stored in the cloud can be shared with researchers while ensuring the privacy of private information and the integrity of EMR. In addition, our scheme can achieve block-level deduplication, in which only one copy of the deduplicated blocks in EMR needs to be stored. The conducted security analysis and performance evaluation affirm the security and efficiency of the proposed scheme.

Safety Monitoring and Auto Alert System in Underground Collieries

In the current scenario, significant mine disasters often occur due to explosions and fires. The occurrence of unexpected underground mine landslides poses a significant threat to miner safety, leading to fatalities. Consequently, the proposed system actively monitors key parameters and issues alerts to miners, aiming to mitigate this risk. The system primarily focuses on monitoring gas levels, air quality, temperature, and vibrations in underground mines, with the data being continuously recorded and stored in the ThingSpeak cloud. Visualization of the data is achieved through field charts created within the designated channel. The cloud-stored data is linked with MathWorks to predict missing values, providing a basis for proactive measures. The predicted missing values are then stored in a separate channel on ThingSpeak for continuous chart visualization. This data, complete with timestamps, can be exported to excel for further analysis and application. The scenario of the coal mine is monitored and regular reports are sent to the personnel’s mail which is automated in Thingspeak. In the event of an explosion, the rescue team is promptly notified through SMS alerts using a GSM modem.

Design of Efficient Storage of Health Care Data in Cloud for Disease Prediction

Design of Efficient Storage of Health Care Data in Cloud for Disease Prediction

On the Model Update Strategies for Supervised Learning in AIOps Solutions

AIOps (Artificial Intelligence for IT Operations) solutions leverage the massive data produced during the operation of large-scale systems and machine learning models to assist software engineers in their system operations. As operation data produced in the field are constantly evolving due to factors such as the changing operational environment and user base, the models in AIOps solutions need to be constantly maintained after deployment. While prior works focus on innovative modeling techniques to improve the performance of AIOps models before releasing them into the field, when and how to update AIOps models remain an under-investigated topic. In this work, we performed a case study on three large-scale public operation data: two trace datasets from the cloud computing platforms of Google and Alibaba and one disk stats dataset from the BackBlaze cloud storage data center. We empirically assessed five different types of model update strategies for supervised learning regarding their performance, updating cost, and stability. We observed that active model update strategies (e.g., periodical retraining, concept drift guided retraining, time-based model ensembles, and online learning) achieve better and more stable performance than a stationary model. Particularly, applying sophisticated model update strategies (e.g., concept drift detection, time-based ensembles, and online learning) could provide better performance, efficiency, and stability than simply retraining AIOps models periodically. In addition, we observed that, although some update strategies (e.g., time-based ensemble and online learning) can save model training time, they significantly sacrifice model testing time, which could hinder their applications in AIOps solutions where the operation data arrive at high pace and volume and where immediate inferences are required. Our findings highlight that practitioners should consider the evolution of operation data and actively maintain AIOps models over time. Our observations can also guide researchers and practitioners in investigating more efficient and effective model update strategies that fit in the context of AIOps.

RRefined and Effective Integration of Access Control Mechanism in Blockchain Enabled DApp

The combination of the Internet of Things (IoT) and aviation integration with the help of satellite and 6G communication technologies has led to the Internet of Unmanned Aerial Vehicles (UAV) or Internet of Drones (IoD). To host and share large amounts of drone data in real time, cloud-based IoD algorithm Cloudbased IoD is the wrong choice to reduce the weight of mobile drones. However, how to protect the sensitive drone at the fair is a matter of curiosity, and it is very difficult to break the environment where drone use is available and limited resources are available. Although our previous work in SPNCE21 (PATLDAC) developed an air-on middle of the main load of based drone data access control system with privacy policy, limited access time and user tracking, it provides conflicting and centralized cloud data storage for cloud environment and access with unreliable data. It is not reliable in terms of data access and tracking of users. To this end, we propose a blockchainbased privacy data access control (BPADAC) scheme for distributed and secure drone data sharing on cloudbased IoDs. Building on the granular, traceable, and privacypreserving drone data access capabilities of our previous work, we extend this by using blockchain and decentralized hash tables (DHT) to provide secure and reliable wireless access to humanmachine data and storage. as well as reliable and limited access guarantee mechanisms for cloud drone data sharing services. We also set up a public and denial user tracking system to prevent user abuse and deny the traitor. Finally, we propose a prototype for security analysis and performance evaluation using smart contracts on the Ethereum blockchain to demonstrate the feasibility of BPADAC. hash table, attributebased ciphertext encryption policy, and blockchain-based privacy-protected enabled and aware data access control Key Words: Blockchain, UAV,IoD, Distributed Hash Table, Ciphertext-Policy-Attribute-Based-Encryption, blockchainbased privacy-aware data access control

THE IMPACT OF NEW MEDIA IN THE CONTEXT OF THE TRANSFORMATION OF THE NETWORK ECONOMY

Today, the processes of globalization in the sphere of economy are largely connected with new media and with digitization. Scholars discuss digital economy, digital capitalism, the issues of formation of the Internet economy, as a result of which the term "network economy" is emerged. The technological basis of the development of the digital economy can be considered artificial intelligence, block chain, cloud data storage systems; these innovations began to enter the economy.