Data Storage In Cloud Computing Research Articles

Secure Data Storage in Cloud Computing Using Code Based McEliece and NTRU Cryptosystems

Secure Data Storage in Cloud Computing Using Code Based McEliece and NTRU Cryptosystems

Multi User Authentication for Reliable Data Storage in Cloud Computing

Today's digital environment, Multi-user authentication plays a crucial role in ensuring data integrity and confidentiality, emphasizing its importance of reliable and secure data storage in cloud computing environments. The exploration extends to the strategies for implementing secure multi-user authentication, encompassing aspects such as password policies, biometric verification, encryption, role-based access control (RBAC), and multi-factor authentication (MFA). The issue of reliable data storage is covered in further detail, on the importance of data availability and integrity. Real-world applications of multi-user authentication and reliable data storage are examine. The paper elucidates how these applications enhance overall security, mitigating risks associated with unauthorized access and cyber threats.The paper concludes by integration of multi-user authentication and reliable data storage is explored through considerations the critical role of multi-user authentication in ensuring reliable data storage in cloud computing such as secure API access, token-based authentication, and adherence to security best practices. Challenges in user authentication are addressed, with solutions proposed for seamless access across cloud platforms, including the adoption of Single Sign-On (SSO), multi-factor authentication, regular security audits, collaboration with cloud security experts, and user education and training. The synthesis of challenges, benefits, drawbacks, and implementation strategies provides organizations with a comprehensive guide for enhancing their data security measures.

A novel hybrid cryptographic framework for secure data storage in cloud computing: Integrating AES-OTP and RSA with adaptive key management and Time-Limited access control

The rapid proliferation of cloud computing has necessitated advanced security mechanisms to safeguard sensitive data stored in remote servers. However, this paradigm shift has raised several security concerns, particularly safeguarding private information stored on faraway cloud servers. This study proposes a ground-breaking hybrid cryptographic framework for the secure data storage requirements of cloud computing. The framework incorporates time-limited access control, adaptive key management, and two strong encryption methods: RSA and Advanced Encryption Standard - One Time Password (AES-OTP). AES-OTP and RSA provide symmetric and asymmetric encryption levels to improve data confidentiality and integrity. With the introduction of an intelligent framework for key creation, distribution, and rotation through the adaptive key management component, the security of cryptographic operations is gradually increased. Additionally, time-limited access control helps to protect data privacy by imposing rigorous temporal constraints on data access and reducing security flaws. The effectiveness of the suggested framework is confirmed by thorough performance assessments, which show astonishing accuracy, precision, recall, and F1-score values of 99.12%, 98.78%, 98.11%, and 98.56%. This demonstrates its outstanding skills in protecting private information from unauthorized access and guaranteeing its secrecy in cloud storage settings.

A Secured Data Storage in Cloud Computing by Using Block Design Based Key Agreement

Cloud computing has emerged as a dominant paradigm for storing and accessing data. In group data sharing scenarios, where multiple users collaborate and exchange sensitive information in the cloud, ensuring secure and efficient key agreement becomes crucial. This paper presents a comprehensive analysis of key agreement mechanisms for group data sharing in cloud computing environments. We explore various cryptographic techniques and protocols specifically designed for establishing secure communication channels among group members. The paper discusses the challenges associated with key agreement in the cloud, proposes potential solutions, and provides insights into the implementation and evaluation of such mechanisms. The proposed framework aims to protect data confidentiality, integrity, and availability, ensuring a robust and reliable cloud storage environment. We explore various cryptographic techniques, including encryption, key management, and authentication mechanisms, and discuss their application in securing cloud-based data storage. The framework addresses common security threats and provides guidelines for implementing a secure cloud storage solution.

A Security Frame work for Data Storage in Cloud Computing by Using Cryptographic Approaches

Cloud computing has revolutionized the way data is stored, processed, and accessed. However, with the widespread adoption of cloud services, ensuring the security and privacy of data has become a paramount concern. This paper presents a comprehensive security framework for data storage in cloud computing, leveraging cryptographic approaches. The proposed framework aims to protect data confidentiality, integrity, and availability throughout its lifecycle in the cloud. It explores various cryptographic techniques, including encryption, access control mechanisms, and key management protocols. Additionally, the paper discusses the challenges and considerations associated with implementing cryptographic security measures in cloud storage and provides recommendations for a robust and efficient security framework.

A comprehensive survey of cloud computing, encompassing architectures, data storage, services, security, and applications

Generally, enterprises store data in internal storage and establish firewalls to protect against intruders attempting to access the data. In cloud computing, data is stored in storage provided by service providers. Information privacy protection involves storing data in encrypted form. The cloud system is responsible for both storage and the encryption or decryption of data, allowing system administrators to access encrypted data and decryption keys. This capability poses a risk to information privacy as it enables unauthorized access to information. This survey discusses Data Storage in Cloud Computing, focusing on the concept of storing encryption and decryption services alongside the storage service. Additionally, the survey examines storage system architecture, security challenges, services, and user application systems to provide a comparison of various cloud models. In addition to the fundamental aspects of data storage and encryption, this survey delves into the evolving landscape of cloud computing, addressing the dynamic nature of storage system architectures. Enterprises increasingly rely on cloud service providers for scalable and flexible storage solutions, prompting a critical examination of the security challenges associated with this paradigm shift. The survey examines how storage services in different cloud models affect user applications and privacy. It stresses the importance of understanding cloud providers' services amidst evolving technology and growing threats. The study delves into trends like edge computing and distributed storage, evaluating their impact on cloud storage security. Overall, it aims to provide a comprehensive overview for enterprises and researchers to make informed decisions on data security in Cloud Computing.

Analysing the Cloud Data Storage Technology and Its Architecture Applications

The idea of using the internet to store data in the cloud is becoming more popular these days. Cloud computing data storage is the most crucial and significant research topic. The article begins with an introduction to cloud computing, storage in the cloud, and architecture for storage in the cloud. GFS (Google towards genuine corporate examples) and HDFS (Hadoop Distributed File System) are the cloud data storage technologies that we examine next (Hadoop Distributed File System) With that, we come to the end of our series. We show how to enhance the conventional method of storing files using the eyeOS Web operating system. Distributed storage and fault-tolerant control are provided by HDFS, a component of Hadoop. Keywords: “Cloud Computing; Cloud Storage; Web Operating System; Distributed File System”

An approach for Preserving Privacy in Public Auditing of Cloud data

Abstract: Cloud users can remotely store their data and appreciate the on demand high quality applications and services from a shared pool of configurable computing resources, regardless of local data storage and maintenance. However, the fact that users no longer have physical possession of the out sourced data makes the data integrity protection in Cloud computing a formidable task, especially for users with constrained computing resources. This paper study the problem of ensuring the integrity of data storage in Cloud Computing. In particular, wet ask of allow in gathird party auditor (TPA), on behalf of the cloud client, to verify the integrity of the dynamic datastored in the cloud. To securely introduce an effective third-party auditor (TPA), and determine various techniques and algorithms for strengthen the Security. Keywords: TPA, Cloud Computing, CSP, RSA

Self-Verifiable Attribute-Based Keyword Search Scheme for Distributed Data Storage in Fog Computing With Fast Decryption

Presently many searchable encryption schemes have been proposed for cloud and fog computing, which use fog nodes (or fog servers) to partly undertake some computational tasks. However, these related schemes still retain cloud servers to undertake most computational tasks, which result in large communication costs between edge devices and cloud servers. Therefore, in this paper we propose a self-verifiable attribute-based keyword search scheme for distributed data storage (SV-KSDS) in full fog computing, where each decryption operation on the data required by a user must meet the negotiated decryption rule between fog servers. Our SV-KSDS scheme first provides attribute-based distributed data storage among fog servers through the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(w, \sigma)$ </tex-math></inline-formula> threshold secret-sharing scheme, where fog servers can provide self-verifiable keyword search and data decryption for terminal users. Compared with the data storage in cloud computing, our scheme extends it to the distributed structure while providing fine-grained access control for distributed data storage through attribute-based encryption. The access control policy of our scheme is constructed on linear secret-sharing scheme, whose security is reduced to the decisional bilinear Diffie-Hellman assumption against chosen-keyword attack and the decisional <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${q}$ </tex-math></inline-formula> -parallel bilinear Diffie-Hellman assumption against chosen-plaintext attack in the standard model. Based on theoretical analysis and practical testing, our SV-KSDS scheme generates less computation and communication costs, which further unloads some computational tasks from terminal users to fog servers so as to reduce computing costs of terminal users.

Smart Iot Lysimetry System by Weighing with Automatic Cloud Computing Data Storage

Smart Iot Lysimetry System by Weighing with Automatic Cloud Computing Data Storage

Home-based (virtual) rehabilitation improves motor and cognitive function for stroke patients: a randomized controlled trial of the Elements (EDNA-22) system

BackgroundHome-based rehabilitation of arm function is a significant gap in service provision for adult stroke. The EDNA-22 tablet is a portable virtual rehabilitation-based system that provides a viable option for home-based rehabilitation using a suite of tailored movement tasks, and performance monitoring via cloud computing data storage. The study reported here aimed to compare use of the EDNA system with an active control (Graded Repetitive Arm Supplementary Program—GRASP training) group using a parallel RCT design.MethodsOf 19 originally randomized, 17 acute-care patients with upper-extremity dysfunction following unilateral stroke completed training in either the treatment (n = 10) or active control groups (n = 7), each receiving 8-weeks of in-home training involving 30-min sessions scheduled 3–4 times weekly. Performance was assessed across motor, cognitive and functional behaviour in the home. Primary motor measures, collected by a blinded assessor, were the Box and Blocks Task (BBT) and 9-Hole Pegboard Test (9HPT), and for cognition the Montreal Cognitive Assessment (MoCA). Functional behaviour was assessed using the Stroke Impact Scale (SIS) and Neurobehavioural Functioning Inventory (NFI).ResultsOne participant from each group withdrew for personal reasons. No adverse events were reported. Results showed a significant and large improvement in performance on the BBT for the more-affected hand in the EDNA training group, only (g = 0.90). There was a mild-to-moderate effect of training on the 9HPT for EDNA (g = 0.55) and control (g = 0.42) groups, again for the more affected hand. In relation to cognition, performance on the MoCA improved for the EDNA group (g = 0.70). Finally, the EDNA group showed moderate (but non-significant) improvement in functional behaviour on the SIS (g = 0.57) and NFI (g = 0.49).ConclusionA short course of home-based training using the EDNA-22 system can yield significant gains in motor and cognitive performance, over and above an active control training that also targets upper-limb function. Intriguingly, these changes in performance were corroborated only tentatively in the reports of caregivers. We suggest that future research consider how the implementation of home-based rehabilitation technology can be optimized. We contend that self-administered digitally-enhanced training needs to become part of the health literacy of all stakeholders who are impacted by stroke and other acquired brain injuries.Trial registration Australian New Zealand Clinical Trials Registry (ANZCTR) Number: ACTRN12619001557123. Registered 12 November 2019, http://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=378298&isReview=true

Multi-Scale Data Center Room for Computing and Storage

In this paper, a list of multi-scale data center system computers and data Storage devices for cloud computing and transfer of cloud computing data and storage and computations in a square 100 meters room is worked for AIDesign software for cloud computing data storage using the devices and computing using the same storage requirements and performing the transfer of data storage and data transfer and computations for AIDesign software to perform the operations, computing, storage and data transfer to enable milliseconds computing of design and analysis in the cloud computing storage facility room. AIDesign software is patented, and industries approved and commercially available over https://aidesign.today.

GreenCloudNet++: Simulation framework for energy efficient and secure, green job scheduling in geographically distributed data centers

AbstractGeographically distributed data centers are used as backbone infrastructure to meet rapidly increasing service demands of computations and data storage in cloud computing. This increase results in high energy consumption, increased operational expenditures, and high carbon footprint which are becoming points of great concern for service providers. In this research work, we present a simulation framework named GreenCloudNet++ for simulation and evaluation of energy‐efficient, green‐aware, and secure job scheduling mechanisms for geographically distributed data centers. GreenCloudNet++ has been developed as an extension to CloudNetSim++, which is designed to simulate distributed data center architectures connected with high speed networks. The functionality of CloudNetSim++ is extended by adding hierarchical job scheduling mechanism, hierarchical statistics collection mechanism and integration mechanism for green energy. Proposed model considers availability of green energy at each data center and maximizes its utilization. It considers the amount of underutilized computational resources at individual data centers while assigning jobs to the data centers, which helps to achieve better server consolidation resulting in better energy efficiency. Proposed model also relies on network load inside each data center which helps avoiding hotspots.

Informatyka kryminalistyczna w kontekście usług przechowywania danych w chmurze obliczeniowej

The role and capabilities of digital forensics in the cloud computing environment still remain an unsolved and insufficiently explored scientific area. The growing popularity of cloud-based data storage services makes recovery of evidence on the Internet a major challenge for forensic IT exminers. Existing legal barriers in most cases prevent direct access to cloud stored resources for the purpose of recovering data. In many situations, assistance from foreign law enforcement and judicial authorities is required. Findings made during forensic IT analysis that allow reconstructing user’s activities and indicate the act of taking advantage of cloud computing data storage is a justification for undertaking international cooperation. Therefore, it is important to know potential sources of information that enable the reconstruction of the activity of the user in cloud computing services, as well as the awareness of the current state of knowledge and scientific research in this field.

CLOUD-DESKTOP REMOTE SENSING DATA MANAGEMENT TO ENSURE TIME SERIES ANALYSIS, INTEGRATION OF QGIS AND GOOGLE EARTH ENGINE

Abstract. Our context research is conducted to investigate the possibility of common application of the remote sensing and ground-based monitoring data to detection and observation of the dynamics and change in climate and vegetation cover parameters. We applied the analysis of the annual graphs of Normalized Difference Water Index to estimate the length and time frames of growing seasons. Basing on previously gained results, we concluded that we can use the Index-based monitoring of growing season parameters as a relevant technique. We are working on automation of computations that can be applied to processing satellite imagery, computing Normalized Difference Water Index time series (in the forms of maps and annual graphs), and estimation of growing season parameters. As currently used data amounts are big (or up-to-big) geospatial data, we use the Google Earth Engine platform to process initial datasets. Our currently described experimental work incorporates investigation of the possibilities for integration of cloud computing data storage and processing with client-side data representation in universal desktop GISs. To ensure our study needs we developed a prototype of a QGIS plugin capable to run processing in GEE and represent results in QGIS.

Homomorphic RASD Framework &amp; Its Use in Data Storage in Cloud Computing

Homomorphic RASD Framework &amp; Its Use in Data Storage in Cloud Computing

An Authorized Public Auditing Scheme for Dynamic Big Data Storage in Cloud Computing

Nowadays, to utilize the abundant resources of cloud computing, most enterprise users prefer to store their big data on cloud servers for sharing and utilization. However, storing data in remote cloud servers is out of user's control and exposes to lots of security problems such data availability, unauthorized access and data integrity, among which data integrity is a challenging and urgent task in cloud computing. Many auditing schemes have been proposed to check the integrity of data in cloud, but these schemes usually have some disadvantages. One is that these auditing schemes cannot check which block is corrupt when the data is not integrated. The other is that there's no efficient authenticated data structure helping to achieve accurate auditing when the data needs to update frequently. To solve the problems, we propose a public auditing scheme for dynamic big data storage in cloud computing. Firstly, we design a dynamic index table, in which no elements need to be moved in insertion or deletion update operations. Secondly, when data in cloud is not integrated, the third-party auditor can detect which block is corrupt. Finally, an authorization is employed between the third party and cloud servers to prevent denial of service attack. The theoretical analysis and the simulation results demonstrate that our scheme is more secure and efficient.

Intrusion Detection and Secure Data Storage in Cloud Computing Using Modified-Ann and Fully Homomorphic Encryption Algorithm

Intrusion Detection and Secure Data Storage in Cloud Computing Using Modified-Ann and Fully Homomorphic Encryption Algorithm

The Use of Biometrics in Multifactor Authentication (MFA) for Cloud Computing Data Storage

The Use of Biometrics in Multifactor Authentication (MFA) for Cloud Computing Data Storage

Verification of data integrity and co-operative loss recovery for secure data storage in cloud computing

In Cloud Computing, the data stored in the external servers may be tampered or deleted by unauthorized persons or selfish Cloud Service Providers (CSPs). Hence, the Cloud Data Owners (CDOs) have to provide assurance to the integrity and correctness of the stored data in the server. In this paper, a Verification of Data Integrity and Co-operative Loss Recovery technique for secure data storage in Cloud Computing is proposed. In this technique, a ciphertext file is split into various cipher blocks and distributed to randomly selected cloud service providers (CSPs) by the cloud data owner (CDO). If a cloud data user (CDU) wants to access any file, the corresponding ciphertext file is reconstructed from the blocks and downloaded by the user. The file can be decrypted if the attribute set of the user matches the access policy of the application. By simulation results, we show that the proposed technique enhances the data integrity and confidentiality.