Cloud Storage Research Articles

Modelling the Impact of Cloud Storage Heterogeneity on HPC Application Performance

Modelling the Impact of Cloud Storage Heterogeneity on HPC Application Performance

MSCFS-RP: A Colored-Petri-Net-Based Analysis Model for Master–Slave Cloud File Systems with Replication Pipelining

As a typical information system, a cloud file system enables the storage, retrieval, and management of data on remote servers or server clusters. The reliable design of such systems is critical to ensure the security of data and availability of services. However, designing correct-by-construction systems is challenging due to the complexity of and concurrency inherent in cloud file systems. Further, existing works on cloud file system analysis often focus on specific systems or lack formal modeling and verification, leading to potential design flaws and security vulnerabilities. To address these issues, we propose MSCFS-RP, which is a formal analysis model based on colored Petri nets. Leveraging the strengths of colored Petri nets in representing diverse information types with colored tokens and defining explicit rules for concurrent interactions, our model captures the writing and reading processes of clients, meta servers, and clusters. With strong formalism and support for verification using CPN Tools, we rigorously evaluate key properties such as replication consistency under various scenarios. The results demonstrate that MSCFS-RP satisfies these properties, validating its effectiveness and trustworthiness in managing information within cloud storage systems.

Confidentiality and privacy of arbitration in the digital era: pies in the sky?

Abstract Confidentiality and privacy are essential components of arbitration, preceding the digital data era. This article discusses how arbitration stakeholders—arbitration institutions and associations, arbitrators, attorneys, and commercial users—have increasingly lost control over confidentiality and privacy in international commercial arbitration by adopting digital technologies provided by Internet giants, such as Microsoft, Google, Dropbox, and Zoom. Arbitration institutions and users have, often unconsciously, defined confidentiality and privacy expansively to promote the wider use of email, cloud storage, and video conferencing platforms in conducting arbitration proceedings. However, they have overlooked the extent to which the use of digital technologies exposes private and confidential information to third parties, including but not limited to Big Data. They have also placed undue faith in the capacity of Internet giants that dominate the Internet to protect highly sensitive arbitration proceedings which those giants commercialize to garner public attention including in arbitration disputes. In responding to this troubling reality, the article examines how to better protect confidentiality and privacy in arbitration, by refining existing protections embodied in international rules and guidelines and choosing domestic forums that favour such protections. It also encourages arbitration stakeholders to update the rules and their application to arbitral proceedings to respond to both intended and unintended violations of commercial and personal data. That strategy includes redressing the downstream disclosure of injurious data, including by choosing legal forums that protect against the use of intelligence to violate private and confidential data.

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.

Technology Adoption for NGO Management: a Case Study of Atma Foundation

Mammoth leaps in technology in recent years have revolutionized industry like never before, leading to what is referred to as the 4th Industrial Revolution, or Industry 4.0. The digital transformation of the world has lent new scope to automation, collaboration and man-machine interfaces. According to Haleem et al. (2023)1, Management 4.0 increases efficiency and reduces human error in management processes, through the use of technological advancements like Artificial Intelligence, Virtual Reality, Internet of Things, Robotics etc. In this background, it is relevant to study how Non-Governmental Organisations are adapting, and how well they are poised to harness the possibilities of management 4.0. In India, new regulations by the Government have brought the onus on Non-Governmental Organisations to streamline their management especially with regard to financial transactions, compliance, monitoring etc. This increases the relevance of digital transformation for NGOs in India, and a study on its possibilities and challenges will help NGOs prepare a sustainable roadmap. This paper uses the single case study method to explore how the technological advancements of industry 4.0 are playing out for Non-Governmental Organisations. According to Gilgun, (1994)2 and Takahashi & Araujo, (2019)3 when it is required to do an intensive study of a phenomenon with the aim of understanding its various aspects and repercussions, a single case study method is a useful tool. The organisation chosen here as the ‘case’ is ATMA Foundation, a non-profit voluntary organisation with 17 years’ history, working on different verticals, and hence having a wide scope for adopting better management systems. The methodology used here consists of interviews, observation and review of organisational documents. A checklist of digital tools commonly used by NGOs was prepared, and it was found that ATMA Foundation already uses or is exploring the use of more than 75% of these. The leadership team also expressed their willingness to adopt new technology. The organisational processes were reviewed and a general framework of management needs was prepared. After consultation with experts in digital technologies, the researcher identified new tools which would greatly enhance the efficiency of the organisation. It was found that the organisation is currently making good use of many tools like MS office, cloud storage, emails, Zoom, Google meet, WhatsApp, Tally Prime, Systools and OneDrive. Some of the new and/or AI powered tools identified as suitable for this organisation include Zoho Creator, Zoho CRM, Zoho Projects, Workday, Big Data, MS Office with copilot, Open AI, Bing and Bard. Challenges identified include initial cost, upskilling of employees, realigning management processes and ethical issues. Solutions to overcome these barriers ranged from software donations and pro-bono consultations to conducting change management sessions for leaders and employees. The study concluded that even small NGOs had huge potential to harness technology for Management 4.0, and the challenges involved could be overcome with foresight and planning.

A Secure and Efficient Data store and Remote Data Integrity Checking in cloud computing Using TPA

A Secure and Efficient Data store and Remote Data Integrity Checking in cloud computing Using TPA

Data-Driven Framework for Cloud Storage Security Optimization: Leveraging Predictive Analytics and Machine Learning to Enhance Threat Detection and Incident Response

As cloud storage adoption accelerates, securing sensitive data against evolving threats, including Advanced Persistent Threats (APTs), Zero-Day Exploits, and Insider Threats, has become paramount. This research introduces a data-driven framework that harnesses predictive analytics, machine learning (ML), and deep learning (DL) techniques to fortify threat detection and incident response in cloud storage environments. By integrating real-time monitoring via Security Information and Event Management (SIEM) systems, anomaly detection using Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), and predictive modeling through Graph-Based Anomaly Detection (GBAD), our framework identifies potential security risks and optimizes countermeasures. Leveraging ML algorithms, such as Random Forest and Support Vector Machines (SVM), our approach analyzes historical incident data, user behavior, and system logs to predict and prevent attacks. Key benefits include proactive security measures, reduced response times via Security Orchestration, Automation, and Response (SOAR), and minimized data breaches through Containerization (Docker) and Serverless Computing (AWS Lambda). This research advances the development of intelligent cloud storage security solutions, ensuring robust protection for sensitive data in cloud-based infrastructure, compliant with PCI-DSS, HIPAA, GDPR, NIST Cybersecurity Framework, and ISO 27001.

Proof of Inaction for outsourced data with exhaustive audit

In the existing environment of cloud storage and distributed hosting solutions, it is imperative to ensure the long-term preservation of inactive data like keyring backups and essential system logs, as these cannot afford the smallest error or data loss during storage. The current methods of subset sampling for data integrity verification fall short of these critical demands. To solve this problem, we introduce Proof of Inaction (PoI), an innovative framework for verifying the integrity of inactive data through verifiable computation principles. First, during the data owner’s exhaustive audit, the storage provider must maintain complete possession of all data to validate storage integrity, which means failing any integrity challenge if even a single bit is missing. Second, PoI enables the verification of storage integrity for both single and multiple copies of data, supporting an unlimited number of challenges in each scenario. Utilizing the co-CDH assumption, we validate the correctness and soundness of our approach with the random oracle model. Both theoretical analysis and empirical evidence suggest that PoI’s challenge and verification process do not escalate in overhead as the size of the challenged files increases, offering an enhancement over traditional data integrity verification methods.

BSA-SGRU-A Novel Deep Learning Framework for Alleviate the Multiple Attacks in IoT-Cloud Environment

The Internet of Things (IoT) has emanated as an innovative technology that grants users to establish connections to external servers located in the cloud and the uninterrupted Internet, allowing them to live more comfortably. As the number of users increases, security and privacy breaches also increase exponentially, as it invests the greatest threats to the IoT networked devices and cloud storage. Though machine and deep learning algorithms have paved the bright light toward the design of intelligent systems to mitigate the attacks, creating effective defenses against numerous assaults remains a significant design challenge for researchers, and accurately predicting these attacks continues to pose an ongoing hurdle. To solve this problem, this paper proposes a hybrid learning model that ensembles the skip connection-based gated recurrent units (Skip-GRU) and bi-layered self-attention networks to predict and mitigate the different attacks. First, Skip-GRU is constructed using residual connections that remove redundant information and capture only global features. Second, novel bi-layered privacy-preserving networks are combined to obtain the spatial and temporal attributes. Finally, softmax is utilized to obtain the prediction results of sample labels. Performance parameters including accuracy, precision, recall, and F1-score are assessed and evaluated during the comprehensive eradication exploration that is conducted utilizing the NSL-KDD and UNSW datasets. By comparing how it performs to other cutting-edge approaches to learning, the one recommended demonstrates its superiority. The technique has proven its efficacy in providing enough security versus various threats, as seen by the results, which show that it has obtained 0.97 accuracy, 0.96 precision, 0.96 recall, and 0.96 F1-score.

Thumbnail-Preserving Encryption Technology Based on Digital Processing

In recent years, the security of cloud storage has become a topic attracting significant attention due to a series of features such as large storage space, high availability, and low cost. Although traditional plain text images can withstand external attacks, the usability of images is completely lost. In order to balance the usability and privacy of images, some scholars have proposed the thumbnail-preserving encryption (TPE) scheme. The ideal TPE algorithm can keep the same thumbnail before and after encryption, which reduces the time cost and strengthens the resistance to attacks, but the existing schemes cannot fulfill the above criteria. In this paper, we propose a new TPE scheme that combines bit-transform encryption and improved hierarchical encryption. By constructing a chaotic system, both encryption and decryption times are shortened, while the randomness of the selected cells is enhanced. In addition, the Hamming distance is introduced to classify and scramble the binary encryption units. The experimental results show that when the number of thumbnail chunks is 16 × 16, the encryption and decryption time decreases to 4 s, and the SSIM value after encryption is close to 1, which indicates that the thumbnail before and after satisfying the encryption basically remains the same, and when the number of chunks is gradually increased, the success rate of the face detection tends to be close to 0. In addition, as the number of experimental iterations increases, the encryption effect improves with an increasing ability to resist attacks.

Tamper Detection and Self-recovery in a Visual Secret Sharing based Security Mechanism for Medical Records.

Medical records contain highly sensitive patient information. These medical records are significant for better research, diagnosis, and treatment. However, ensuring secure medical records storage is paramount to protect patient confidentiality, integrity, and privacy. Conventional methods involve encrypting and storing medical records in third-party clouds. Such storage enables convenient access and remote consultation. This cloud storage poses single-point attack risks and may lead to erroneous diagnoses and treatment. To address this, a novel (n,n)VSS scheme is proposed with data embedding, permutation ordered binary number system, tamper detection, and self-recovery mechanism. This approach enables the reconstruction of medical records even in the case of tampering. The tamper detection algorithm ensures data integrity. Simulation results demonstrate the superiority of proposed method in terms of security and reconstruction quality. Here, security analysis is done by considering attacks such as brute force, differential, and tampering attacks. Similarly, the reconstruction quality is evaluated using various human visual system parameters. The results show that the proposed technique provides a lower bit error rate ( ≈ 0), high average peak signal-to-noise ratio ( ≈ 35 dB), high structured similarity ( ≈ 1), high text embedding rate ( ≈ 0.7 BPP), and lossless reconstruction in the case of attacks.

Deduplication on Encrypted Data in Cloud Computing

Cloud storage is a crucial component of cloud computing, allowing users to expand their storage without upgrading their equipment and overcome resource constraints. Cloud users' data is always encrypted before being outsourced to ensure their security and privacy. However, encrypted data may result in a significant waste of cloud resources. Storage complicates data sharing among authorized users. We continue to face issues with encrypted data storage and deduplication. Traditional deduplication strategies are designed for certain application settings where data owners or cloud servers have full control over the process. They cannot meet data owners' varying requests based on data sensitivity. This study proposes a flexible data storage management method that combines deduplication and access control across various Clouds. Service Providers (CSP). We assess its performance through security analyses, comparisons, and implementations. The results demonstrate security, effectiveness, and efficiency for actual applications.

Monitoring and Predicting African Rural Household Air Pollution Using Internet of Things and Artificial Intelligence

Abstract According to a 2020 report from the World Health Organization (WHO), household air pollution has led to over 3 million deaths globally, with recent statistics showing a worsening situation in Africa. Integrating Internet of Things (IoT) and Artificial Intelligence (AI) technologies can help address this global challenge. IoT enables real-time data collection for monitoring pollution levels, while AI algorithms predict pollution before it reaches hazardous levels. However, existing solutions are not tailored to the African context, where wood fuel is a primary pollutant, and they predominantly focus on monitoring rather than prediction. This study presents the design and implementation of an IoT-based solution for monitoring and predicting indoor air pollution in rural African households. The system collects data in real time and transmits it to the cloud for storage, processing, and analysis, with alerts to users when pollution is detected. An AI model was successfully trained and tested to predict indoor air pollution based on the collected data. The results indicate that this approach significantly improves the accuracy and timeliness of pollution alerts, potentially reducing health risks associated with indoor air pollution. The successful implementation and testing of the system demonstrate its potential for broader applications in various indoor environments.

Efficient and robust disaster recovery system using cloud-based algorithms with data integrity

Incorporating cloud-based algorithms for disaster recovery (DR), it explores data replication, failover, virtual machine (VM) migration, and consistency algorithms. These algorithms play a pivotal role in safeguarding data and system continuity during unforeseen disruptions. Data replication ensures redundancy, failover algorithms swiftly transition to backup resources, VM migration facilitates resource optimization, and consistency algorithms maintain data integrity. Leveraging cloud technology enhances the effectiveness of these algorithms, providing robust DR solutions critical for business continuity in today's digital landscape. The recent growth in popularity of internet services on a massive scale has also raised the demand for stable underpinnings. Despite the fact that DR for big data is frequently overlooked in security research, the majority of existing approaches use a narrow, endpoint-centric approach. The significance of DR strategies has grown as cloud storage has become the norm for more data. But traditional cloud-centric DR techniques may be expensive, thus less expensive alternatives are being sought. There is persistent concern in the information technology (IT) community about whether or not cloud service providers (CPs) can guarantee data and service continuity in the event of a disaster.

A Grand Challenge: Digital Transformation for the Upstream Oil and Gas Industry

_ This is the fourth in a series of six articles on SPE’s Grand Challenges in Energy, formulated as the output of a 2023 workshop held by the SPE Research and Development Technical Section in Austin, Texas. Described in a JPT article last year, each of the challenges will be discussed separately in this series: geothermal energy; net-zero operations; improving recovery from tight/shale resources; digital transformation; carbon capture, utilization, and storage; and education and advocacy. _ The interval from the oil price decline of 2014–2015 through the global COVID-19 pandemic of 2020–2022 was a difficult period for the upstream oil and gas business. The industry has responded, as so many times before, by seeking to reduce structural costs with the minimum impact to required new investments, including those in nondiscretionary domains (security, including cybersecurity, and safety, health, and environmental performance) as well as those required to appropriately hedge against the emergence of new, more sustainable energy sources. Digital transformation is one of the key opportunities for the industry to systematically reduce costs across multiple business domains. While there has been active discussion of the “digital oil field” for several decades, the economic shock of the past decade revived interest in the application of digital technologies across the enterprise to improve the cost structure of businesses. Of course, at the same time, entirely new industries, such as social media, have emerged exploiting new digital technologies, including machine learning, web software and connectivity, and cloud storage and computing. These technologies have also transformed traditional industries such as retail and manufacturing. But taking the same path in the upstream industry has some special challenges and opportunities. This review focuses on digital transformation opportunities that are, to a greater or lesser degree, unique to the special data and operating environment of the modern petroleum industry. There are many valuable opportunities, including business process automations, that can and should be adopted by upstream companies, but are not different in kind from such innovations in other industries. Data Is Not the New Oil One challenge is the sheer mass and heterogeneity of upstream data. The upstream oil and gas industry is an old industry, with both business and technical records stretching back for many companies to the 19th century. These can be in paper form or a variety of digital forms reflecting the different methods of storing data since computers became a standard business and scientific tool in the 1950s. Conversion of this data to more modern formats suitable for cloud storage, for example, is not cost-free. Thus, a key principle in digital transformation is that it must be value-driven. A frequently used phrase, “data is the new oil,” is, at least for the oil and gas business itself, deeply misleading. Most of the data held by any enterprise, be it in libraries, shared data servers, or personal hard drives, is not economically useful. Much other data, while potentially valuable, requires such substantial spending on quality assurance, data curation, data transfer to points of computation, and use of (highly paid) domain experts to interpret data analysis and make business decisions that there is no pathway to reliable value based on the data.

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

Authorized Redundant Check Support in a Hybrid Cloud Environment

A hybrid cloud is made up of public and private clouds connected by standardized or proprietary technologies to provide data and application mobility. The deduplication with uneven privileges issue in cloud computing is addressed efficiently. an architecture for a hybrid cloud that employs both public and private clouds, with the data owners only using the public cloud for storage and the private cloud for data administration. To make data management in cloud computing scalable, duplication has lately become a commonly employed method. Deduplication reduces the amount of bandwidth you need, speeds up data transfers, and keeps your need for cloud storage to a minimum. To enable authorized duplicate checks in hybrid cloud architecture, the proposed system comprises a variety of new deduplication structures. Data confidentiality was protected before outsourcing by employing the convergent encryption method. A system authorized to use deduplication supports differential authorization duplicate checks. In the authorized duplication check approach, testbed tests are carried out using a prototype as proof of concept.

Cloud Forensic Artefacts: Digital Forensics Registry Artefacts discovered from Cloud Storage Application

Cloud Forensic Artefacts: Digital Forensics Registry Artefacts discovered from Cloud Storage Application

Predictive-Cognitive Maintenance for Advanced Integrated railway Management

Railway systems play a vital role in modern transportation and Predictive-Cognitive Maintenance (PCM) has emerged as a transformative approach in the context of Advanced Integrated Railway Management for ensuring the safety, reliability, and efficiency of these systems. PCM leverages data analytics and machine learning to optimize railway system maintenance. This requires effective structural health monitoring (SHM) using low-cost sensor devices. This paper presents a prototype solar-powered wireless sensor node with a 3-axis MEMS accelerometer and energy-harvesting features for monitoring rail track vibrations. The node contains a microcontroller that runs embedded machine-learning models to preprocess the vibration data after train crossing. Abnormal vibrations, indicative of defects, were detected in real time using the TinyML inference at the edge. Instead of raw data, only the model results were wirelessly transmitted to a digital twin in the cloud. The digital twin aggregates data across the rail network for system-level assessment of the RUL and maintenance planning. This edge computing approach minimizes wireless transmission and cloud storage compared to raw sensor streaming. Embedded ML enables real-time damage detection, whereas the cloud digital twin enables system-level prognosis insights. The solar-powered platform enables long-term remote monitoring at low cost without wiring or battery changes. A full-scale physical model was used to validate the edge node prototypes against calculation models and wired accelerometers for impulse loads. The results demonstrate that these nodes can provide a sensor layer for cost-effective PCM in railway systems. In summary, this work proposes an edge computing and embedded ML approach for SHM that integrates cloud-based digital twins to enable the predictive-cognitive maintenance of railway infrastructure. Wireless nodes demonstrate potential for low-cost, convenient, and automated rail health monitoring.

Identity-Based Data Auditing Scheme With Provable Security in the Standard Model Suitable for Cloud Storage

Identity-Based Data Auditing Scheme With Provable Security in the Standard Model Suitable for Cloud Storage