Cloud Storage System Research Articles

DSDOS Cloud: A Decentralized Secure Data Outsourcing System With Hybrid Encryption, Blockchain Smart Contract‐Based Access Control, and Hash Authentication Codes for Cloud Security

ABSTRACTWith the increasing trend of outsourcing data to cloud services, ensuring data security and privacy has become crucial. Typically, data are stored on cloud servers in encrypted form to mitigate risks. However, accessing the encrypted data requires an access key distributed by a third party. If this third party is untrustworthy, it poses a significant security threat to the system. To address this challenge, we propose a Decentralized Secure Data Outsourcing System (DSDOS) that uses blockchain technology to ensure data security and privacy. The DSDOS system comprises three modules: data security and privacy, access control and authorization, and data integrity and availability. The data security and privacy module uses a hybrid encryption scheme that combines Advanced Encryption Standard (AES), partially homomorphic encryption (PHE), and Diffie–Hellman (DH) to ensure secure data storage and access. The access control and authorization module uses a blockchain‐based smart contract system to manage access to the encrypted data. The data integrity and availability module uses hash‐based message authentication code (HMAC) to ensure that the data are not tampered with and is always available. We conducted a security and performance analysis of the DSDOS system and found that it outperforms previous schemes in terms of security and performance. The DSDOS system is a secure and privacy‐preserving data outsourcing system that can be used to mitigate the security risks associated with traditional cloud storage systems.

RESIST: Randomized Encryption for Deduplicated Cloud Storage System

RESIST: Randomized Encryption for Deduplicated Cloud Storage System

Usability and Challenges of Cloud Storage: Perspectives of Academic Staff at Mzumbe University, Tanzania

This study aimed to explore the perspectives, preferences and challenges of Mzumbe University faculty regarding cloud storage adoption. Utilizing a case study design, the researchers conducted semi-structured interviews with a purposive sample of 15 faculty members to capture diverse viewpoints. The study used the thematic analysis approach to analyse the data retrieved from interviews. Based on the findings, the study concluded that cloud storage systems have several advantages, such as easily accessibility features, robust security protocols, service compatibility and intuitive user interfaces. These benefits encourage academic personnel to collaborate and increase productivity. However, some challenges existed. These related with internet access, cost, lack of formal training and security. The study recommends that enhancing the university internet infrastructure is critical to guaranteeing reliability and fast connectivity for effective utilization of cloud storage. The university may also thing of offering financial assistance for supplementary storage requirements to academic staff.

An Optimized Encryption Storage Scheme for Blockchain Data Based on Cold and Hot Blocks and Threshold Secret Sharing.

In recent years, with the rapid development of blockchain technology, the issues of storage load and data security have attracted increasing attention. Due to the immutable nature of data on the blockchain, where data can only be added and not deleted, there is a significant increase in storage pressure on blockchain nodes. In order to alleviate this burden, this paper proposes a blockchain data storage strategy based on a hot and cold block mechanism. It employs a block heat evaluation algorithm to assess the historical and correlation-based heat indicators of blocks, enabling the identification of frequently accessed block data for storage within the blockchain nodes. Conversely, less frequently accessed or "cold" block data are offloaded to cloud storage systems. This approach effectively reduces the overall storage pressure on blockchain nodes. Furthermore, in applications such as healthcare and government services that utilize blockchain technology, it is essential to encrypt stored data to safeguard personal privacy and enforce access control measures. To address this need, we introduce a blockchain data encryption storage mechanism based on threshold secret sharing. Leveraging threshold secret sharing technology, the encryption key for blockchain data is fragmented into multiple segments and distributed across network nodes. These encrypted key segments are further secured through additional encryption using public keys before being stored. This method serves to significantly increase attackers' costs associated with accessing blockchain data. Additionally, our proposed encryption scheme ensures that each block has an associated encryption key that is stored alongside its corresponding block data. This design effectively mitigates vulnerabilities such as weak password attacks. Experimental results demonstrate that our approach achieves efficient encrypted storage of data while concurrently reducing the storage pressure experienced by blockchain nodes.

Chaos based image encryption scheme to secure sensitive multimedia content in cloud storage

Cloud computing offers a variety of on-demand services to users and has gained significant prominence in the contemporary era. The security of information stored in cloud data centers has become a central concern, especially for sensitive data like medical images, videos, and multimedia content that require heightened protection when stored in data centers. Cloud users have a responsibility to ensure the security of their data through strategic measures. Focusing on maintaining the privacy of images stored in the cloud, this research introduces an innovative image encryption technique that is based on a modified Skew Tent chaotic map. The suggested modification to the chaotic map includes combining the Skew Tent map with both the sine trigonometric function and perturbation technology. This results in enhanced randomness, more intricate dynamical behavior, a broader chaotic range, and increased sensitivity to initial values in contrast to alternative chaotic maps. The incorporation of this adapted map into a stepwise procedure, involving two rounds of permutation followed by diffusion, effectively accomplishes image data encryption for cloud storage systems. These consecutive operations collectively enhance the encryption method’s randomness and robustness. Through simulations conducted using Cloudsim, the cipher-image exhibits a uniform distribution and achieves a commendable information entropy score of 7.996749. The encryption algorithm significantly reduces correlation coefficients from 1 in the original image to 0 in the encrypted image, while maintaining NPCR (Number of Pixel Change Rate) and UACI (Unified Average Changing Intensity) values within the critical range. Additionally, both theoretical analysis and practical evaluations confirm the algorithm’s resilience against exhaustive, occlusion, and classical attacks. Moreover, extending this encryption framework to video data, a novel video encryption approach is proposed.

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.

Proactive Drive Failure Prediction for Cloud Storage System Through Semi-Supervised Learning

Proactive drive failure prediction can help operators handle the failing drives in advance, enhancing the storage system dependability. SSD and HDD failure prediction techniques are currently evolving towards a semi-supervised approach. In this paper, we are dedicated to enhancing the methodology for semi-supervised drive failure prediction from these aspects: design more powerful, robust yet generic models, mine drive failure modes and make the prediction model interpretable. Specifically, we propose two semi-supervised drive failure prediction models, DFP-VL and DFP-FL, from the perspective of reconstructing SMART data and learning SMART data's probability density, respectively. They capture the pattern of healthy drives, and failing drives can be detected when they deviate from the normal pattern. We mine two failure modes, slow deterioration and dramatic deterioration. Based on that, we design two failure detectors, ”ThresholdDetector” and ”HybridDetector”, to determine whether a drive deviates from the normal pattern. We evaluate the proposed methods on both SSD and HDD SMART data. DFP-VL is generic yet effective and can interpret the predicted results. DFP-FL has better performance than DFP-VL, but it cannot interpret the results. ThresholdDetector has low complexity and can detect most failing drives. HybridDetector has high complexity and can further improve the detection performance.

Secure data sharing scheme with privacy-preserving and certificateless integrity auditing in cloud storage

With cloud storage services becoming wildspread and low-cost, individuals and organizations select outsourcing large amounts of document to cloud. Cloud storage applications provide data sharing services with varying functionality. In some cloud storage systems that provide data sharing, such as e-health systems, hiding sensitive information fields is a necessity. Meanwhile, in sharing process, unauthorized entities may have access to these privacy fields. The resource-constrained computation capability for data owner (DO) is universal. To tackle above problems, we propose a sanitizer-based secure data sharing scheme where sanitizer performs most computations of encryption and signature after receiving the partially blinded data from DO. It also checks the legitimacy of visitors before CSP returns stored data file. Besides, adopting an attribute-based access policy guarantees further privacy and fine-grained access authorization. After verification, cloud will return the required data to the legitimate visitors. Moreover, integrity auditing to shared data file is based on certificateless authentication technique, which removes certificates issue of traditional cryptographic technique, avoids key-escrow attack risk caused by identity-based cryptography. Finally, the performance analysis and experimental results show that our proposed scheme is competitive in practical applications since it reduces the authenticator generation overhead by up to 21.6% and proof generation overhead by up to 12% compared with related schemes.

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.

Convergent encryption enabled secure data deduplication algorithm for cloud environment

SummaryThe exponential growth of data poses a critical challenge for cloud storage systems. Redundant data consumes valuable storage space and increases infrastructure costs. Data deduplication, a technique for eliminating duplicate data copies, offers a promising solution. However, existing deduplication techniques often compromise data security, especially when dealing with encrypted data. This paper proposes a novel approach that merges convergent encryption (CE) with data deduplication. CE leverages user data itself to generate unique encryption keys, enabling secure deduplication on encrypted data. We analyze existing literature on secure data deduplication and categorize various techniques using UML activity diagrams. We then present our proposed CE‐based deduplication system, outlining its functionalities through UML diagrams. This research contributes to the field of secure data storage by proposing a novel and secure deduplication approach. By demonstrating its efficiency and security benefits, this work paves the way for more efficient and secure cloud storage solutions. Finally, we demonstrate the system's effectiveness through a comparative analysis, highlighting its potential to significantly improve storage efficiency while maintaining data security.

Decentralized file storage using Blockchain

Amidst the exponential growth in data volumes and growing apprehensions regarding centralized control, decentralized cloud storage systems have emerged as a beacon of promise. This study presents a comprehensive examination of these systems, delving into their structural intricacies, benefits, and hurdles. The essence of decentralized cloud storage lies in dispersing data across a network of nodes, obviating the necessity for a central governing entity. Through the utilization of blockchain technology and cryptographic methods, information is encrypted, fragmented, and dispersed among myriad nodes, ensuring both redundancy and heightened security. This framework effectively mitigates the inherent risks of data loss or tampering associated with centralized infrastructures, thus bolstering data integrity and confidentiality. In summary, decentralized cloud storage systems signify a revolutionary shift in data management paradigms, furnishing a resilient, scalable, and secure alternative to centralized storage models. Ongoing research and development endeavours are imperative to surmount extant challenges and fully harness the transformative potential of this groundbreaking technology. Keywords— - Blockchain, Data Security, IPFS, Encryption, File Storage, Decentralized storage, Metamask, Pinata.

EFFICIENT MULTI-AUTHORITY ACCESS CONTROL USING CIPHERTEXT POLICY -HIERARCHICAL ATTRIBUTE BASED ENCRYPTION IN CLOUD STORAGE

The goal of this research is to improve the security of cloud-based data storage systems by presenting the Cloud Secure Storage Mechanism based on Data Dispersion and Encryption (CPHABE). The User Frame, Authority Login, Cloud Server Login, and Data Owner Login are the four separate components that make up the system. By providing file names and requesting transformation keys from the authority for decryption, users start downloads. Requests for transformation keys, upload information, and key creation are handled by the authority. Owner identities, file names, encrypted content, gate types, and storage size graphs are all managed by cloud servers. Partially decrypted data, such as user identities, file names, transformation keys, data owner IDs, and partially decrypted content, is sent in response to user requests. Data owners may use the system to explore files, obtain keys, and apply hybrid encryption methods including Authenticated Encryption (AE) and Key Encapsulation Mechanism (KEM). This all-encompassing strategy guarantees strong encryption, data dispersion, and security measures, offering a stable foundation for cloud storage systems. Keywords: Cloud Storage, Security Mechanism, Encryption

BLOOM : CLOUD OPTIMIZATION TOOL

Bloom: Cloud Optimization tool" endeavors to harness the power of the Ant Colony Optimization (ACO) algorithm to address the challenge of organizing unstructured input data in cloud storage systems. The primary objective is to optimize cloud storage utilization and minimize associated billing expenses. Through the utilization of ACO, the solution aims to transform unorganized and unstructured data into a structured format, facilitating efficient storage allocation and enhancing data management practices. By leveraging predictive insights derived from the structured data, the solution empowers both cloud service providers and users to make informed decisions regarding data storage optimization. Ultimately, this approach not only streamlines data organization processes but also aids in maximizing cost-effectiveness by ensuring that cloud resources are allocated optimally based on usage patterns and predicted future demands. The integration of ACO into the solution offers a promising avenue for addressing the complexities inherent in managing vast amounts of unstructured data in cloud environments, thereby paving the way for improved efficiency and cost savings.

Prediction of Diabetes Using Machine Learning Algorithms

In the medical field, it is essential to predict diseases early to prevent them. Diabetes is one of the most dangerous diseases all over the world. In modern lifestyles, sugar and fat are typically present in our dietary habits, which have increased the risk of diabetes. To predict the disease, it is extremely important to understand its symptoms. Currently, machine-learning (ML) algorithms are valuable for disease detection. This article presents a model using a fused machine learning approach for diabetes prediction. The conceptual framework consists of two types of models: Support Vector Machine (SVM) and Artificial Neural Network (ANN) models. These models analyze the dataset to determine whether a diabetes diagnosis is positive or negative. The dataset used in this research is divided into training data and testing data with a ratio of 70:30 respectively. These three models gave us different accuracies and simple to use. A cloud storage system stores the fused models for future use. Based on the patient’s real-time medical record, the fused model predicts whether the patient is diabetic or not. The proposed fused ML model has a prediction accuracy, which is higher than the previously published methods. Keywords: Logistic Regression, SVM, ANN, ML techniques

INTERFACES OF VIRTUAL DATA STORAGE IN THE CONDITIONS OF A FAST-CHANGING INFORMATION ENVIRONMENT

The article analyzes data storage architectures, explores the main approaches to cloud technologies, and highlights the features of virtual data storage architecture formation. In the process of analysis, positive and negative features of individual data storage architectures were highlighted. Cloud information technologies are considered from the point of view of their use for data storage and distribution. When considering cloud data storage, attention is focused on their architectural features from the point of view of efficiency and availability for users. Storage efficiency is an important characteristic of cloud storage infrastructure, especially given its emphasis on overall cost savings. Performance has many aspects, but the main task of a cloud storage system is to move data between the user and the remote cloud service provider. The system architecture was designed based on the proposed and modeled methods of organizing access to the cloud storage. According to the designed architecture, the data flows in the system were analyzed and investigated. The developed algorithm for the collection and integration of disparate data based on virtual data warehouses, in contrast to the Inmon and Kimball models, makes it possible to process NoSQL temporal (InfluxDB), graph (Neo4j) and document-oriented (MongoDB) data with a significantly lower value of capacity complexity and the possibility execution of recursive queries. The developed interface should be able to work with relational, temporal, graph and document-oriented data sources. To do this, it must provide the ability to perform SQL-like queries against relational databases, as well as specialized queries for each type of non-relational data. It allows to increase the efficiency of process0ng both relational and non-relational data, which makes it a powerful tool for analyzing and processing information from various sources. The development of virtual data warehouses may be associated with the introduction of new technologies, such as artificial intelligence, blockchain, quantum computing, etc. In addition, further improvements in metadata management and data security are expected.

SECURE CLOUD STORAGE PROTOCOL WITH DATA DYNAMICS USING DSCS PROTOCOL

The goal of the project "Secure Cloud Storage Protocol With Data Dynamics Using DSCS Protocol" is to create a cutting-edge cloud storage protocol that cleverly integrates network coding technologies to combine efficiency and security. The goal of this project is to provide strong protection against data loss and illegal access while improving the performance and dependability of cloud storage systems. The suggested protocol distributes and encrypts data among several cloud network nodes using network coding techniques. This strengthens the system against possible data loss or corruption by introducing redundancy and error correction in addition to optimizing data transit and storage efficiency. Key Words: cloud, private key, sensitive data, Network Coding

TALICS3: Tape library cloud storage system simulator

High performance computing data is surging fast into the exabyte-scale world, where tape libraries are the main platform for long-term durable data storage besides high-cost DNA. Tape libraries are extremely hard to model, but accurate modeling is critical for system administrators to obtain valid performance estimates for their designs. This research introduces a discrete–event tape simulation platform that realistically models tape library behavior in a networked cloud environment, by incorporating real-world phenomena and effects. The platform addresses several challenges, including precise estimation of data access latency, rates of robot exchange, data collocation, deduplication/compression ratio, and attainment of durability goals through replication or erasure coding. Using the proposed simulator, one can compare the single enterprise configuration with multiple commodity library configurations, making it a useful tool for system administrators and reliability engineers. This makes the simulator a valuable tool for system administrators and reliability engineers, enabling them to acquire practical and dependable performance estimates for their enduring, cost-efficient cold data storage architecture designs.

Smart Contracts for Ensuring Data Integrity in Cloud Storage with Blockchain

INTRODUCTION: Data integrity protection has become a significant priority for both consumers and organizations as cloud storage alternatives have multiplied since they provide scalable solutions for individuals and organizations alike. Traditional cloud storage systems need to find new ways to increase security because they are prone to data modification and unauthorized access thus causing data breaches.
 OBJECTIVES: The main objective of this study is to review usage of smart contracts and blockchain technology to ensure data integrity in cloud storage.
 METHODS: . Case studies, performance evaluations, and a thorough literature review are all used to demonstrate the effectiveness of the suggested system.
 RESULTS: This research has unveiled a revolutionary approach that capitalizes on the fusion of smart contracts and cloud storage, fortified by blockchain technology.
 CONCLUSION: This theoretical analysis demonstrate that smart contracts offer a dependable and scalable mechanism for maintaining data integrity in cloud storage, opening up a promising area for further research and practical application.

SECURED ORGANIZATIONAL CLOUD STORAGE USING MESSAGE DIGEST ALGORITHM

The Secure Organizational Cloud Storage System is an advanced cloud storage platform designed specifically for organizations that prioritize data security. The platform provides a similar functionality to Google Drive, but with enhanced security features to ensure that sensitive information remains safe from cyber threats. With the Secure Organizational Cloud Storage System, organizations can store and manage their data in a centralized and secure location. Users can easily upload, share, and collaborate on files and documents, and can access them farom anywhere with an internet connection. Secure cloud storage is indispensable in today's data-driven world, providing the convenience of remote data access while mitigating the risks associated with cyber threats and data breaches. By implementing advanced encryption, access controls, data integrity checks and redundancy strategies, secure cloud storage systems offer a comprehensive solution to protect sensitive information in an increasingly interconnected digital ecosystem. This abstract serves as a concise introduction to the importance and key components of secure cloud storage. Cloud service and storage providers offer valuable IT solutions for businesses of all sizes. Originally thought of as more for personal use, cloud storage for business is following in the footsteps of many personal technologies adapted for business purposes. Cloud storage works by storing data on remote servers, where it can be maintained, managed, backed up and accessed remotely. Data stored in the cloud is accessible by any device at any time, as long as permissions are in place. Despite its accessibility, data stored via the cloud is extremely safe and secure. Many people reap the benefits of the cloud for personal reasons, but most businesses have yet to take the leap. Whether your current on-premises data storage seems sufficient, or you simply haven’t taken time to consider cloud storage, reviewing these advantages is a good place to start.

Blockchain assisted blind signature algorithm with data integrity verification scheme

SummaryAs the demand for cloud storage systems increases, ensuring the security and integrity of cloud data becomes a challenge. Data uploaded to cloud systems are vulnerable to numerous sorts of assaults, which must be handled appropriately to avoid data tampering issues. In addition, quantum computers are expected to be introduced soon, which may face multiple security issues by destroying all traditional cryptosystems. This work introduces a quantum‐resistant blockchain centered data integrity verification system with the use of several techniques. Initially, the keys and signatures are generated by the users with the help of the lattice‐based blind signature algorithm (L_BSA), which is a combination of lattice cryptography and a blind signature algorithm. From the generated random keys, the most optimal key is then selected by the Puzzle Optimization Algorithm (POA), which is then made available to the encryption phase. Then, the upgraded Merkle tree‐assisted vacuum filter (Vac‐UMT) algorithm is executed to accomplish the encryption task. Then the data are converted into blocks using blockchain technology and uploaded to the cloud. When receiving the audit requests, the verification process is carried out, and the evidence report is generated for the users. The proposed work is simulated in JAVA and assessed with the UNSW‐NB15 dataset, and the outcomes demonstrated that the system is highly efficient and secure.