Relevant bibliographies by topics / Cloud data storage

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Cloud data storage'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Cloud data storage"

1

A.Mounika, A. Mounika, and C. Srinivas C.Srinivas. "Enabling Dynamic Data In Cloud Storage." International Journal of Scientific Research 1, no. 5 (June 1, 2012): 25–27. http://dx.doi.org/10.15373/22778179/oct2012/9.

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2

Aiyer, Viswanath, Rohit Bhutkar, Sagar Anvekar, and Dinesh Chavan. "Guaranteeing Data Storage Security in Cloud Computing." International Journal of Engineering Research 4, no. 5 (May 1, 2015): 231–34. http://dx.doi.org/10.17950/ijer/v4s5/504.

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3

Mahida, Ankur. "Secure Data Outsourcing Techniques for Cloud Storage." International Journal of Science and Research (IJSR) 13, no. 4 (April 5, 2024): 181–84. http://dx.doi.org/10.21275/sr24402065432.

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4

Yolchuyev, Agil, and Janos Levendovszky. "Data Chunks Placement Optimization for Hybrid Storage Systems." Future Internet 13, no. 7 (July 11, 2021): 181. http://dx.doi.org/10.3390/fi13070181.

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“Hybrid Cloud Storage” (HCS) is a widely adopted framework that combines the functionality of public and private cloud storage models to provide storage services. This kind of storage is especially ideal for organizations that seek to reduce the cost of their storage infrastructure with the use of “Public Cloud Storage” as a backend to on-premises primary storage. Despite the higher performance, the hybrid cloud has latency issues, related to the distance and bandwidth of the public storage, which may cause a significant drop in the performance of the storage systems during data transfer. This issue can become a major problem when one or more private storage nodes fail. In this paper, we propose a new framework for optimizing the data uploading process that is currently used with hybrid cloud storage systems. The optimization is concerned with spreading the data over the multiple storages in the HCS system according to some predefined objective functions. Furthermore, we also used Network Coding technics for minimizing data transfer latency between the receiver (private storages) and transmitter nodes.
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XIE, Hua-cheng, and Xiang-dong CHEN. "Cloud storage-oriented unstructured data storage." Journal of Computer Applications 32, no. 6 (August 24, 2013): 1924–28. http://dx.doi.org/10.3724/sp.j.1087.2012.01924.

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Bhavani, S. Durga, Gudlanarva Sudhakar, and Ujjwal Karna. "Data Storage Security in Cloud Computing: A Survey." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 1 (January 30, 2017): 52–57. http://dx.doi.org/10.23956/ijarcsse/v7i1/0148.

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Jaikar, S. P. "Securing Cloud Data Storage." IOSR Journal of Computer Engineering 1, no. 6 (2012): 43–49. http://dx.doi.org/10.9790/0661-0164349.

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Ekta, Mrinal. "Secured Cloud Data Sharing: Privacy-Preserving Storage Optimization with Data Confidentiality." International Journal of Research Publication and Reviews 4, no. 8 (August 2023): 2957–66. http://dx.doi.org/10.55248/gengpi.4.823.51935.

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K L, Anitha, and T. R. Gopalakrishnan Nair. "Data storage lock algorithm with cryptographic techniques." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (October 1, 2019): 3843. http://dx.doi.org/10.11591/ijece.v9i5.pp3843-3849.

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<span>The cloud computing had its impact far and wide, and Enterprise solutions are getting migrated to different types of clouds. The services are delivered from the data centers which are located all over the world. As the data is roaming with less control in any data centers, data security issues in cloud are very challenging. Therefore we need multi-level authentication, data integrity, privacy and above all encryption to safeguard our data which is stored on to the cloud. The data and applications cannot be relocated to a virtual server without much degree of security concern as there can be much confidential data or mission-critical applications. In this paper, we propose Data Storage Lock Algorithm (DSLA) to store confidential data thereby provides secure data storage in cloud computing based on cryptographic standards.</span>
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Naga Chandrika H, Et al. "Data Security on Backed Up Data and Recovery in Cloud Storage." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (November 5, 2023): 4563–74. http://dx.doi.org/10.17762/ijritcc.v11i9.9971.

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Cloud provides its users with different services. Every day during cloud computing, a great deal of data is generated. On the cloud servers, the data is being saved. A recovery tool should be created in case this data is lost from the server. One such setup is described in this consideration. The proposed approach would enable simultaneous data storage on the inaccessible server and the cloud server. The information is returned from the farther server when the key record is misplaced. Secret key security is ensured so that the authentication is secure and authorized by the user based on the attributes of backup and recovery. A cloud is a distinct Information Technology infrastructure designed to provide its users with different facilities that can be gotten to remotely. Cloud alludes to the term arrange of systems that back get to decentralized Data Innovation assets. Cloud employments the Web as well as the inaccessible central servers to manage consumers and businessmen's data and applications. This helps to save the consumer from costs and room problems. It is a technology that makes data collection, processing, and bandwidth much more centralized. A cloud encompasses a certain restrain, because it could be a certain system utilized to supply assets remotely. The Web offers get to an endless number of clouds. Though the Web offers free get to a few web-based Information Technology services, a cloud is generally private and provides wireless data resources. Much of the Internet is accessible via the web service to Information Technology services. On the other hand, the Information Technology services supplied by cloud environments are intended to provide back-end computing capabilities and browser access..
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Dissertations / Theses on the topic "Cloud data storage"

1

Avlasovych, V. V. "Cloud data Storage." Thesis, Sumy State University, 2016. http://essuir.sumdu.edu.ua/handle/123456789/46877.

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Today, the Internet gives us a lot of opportunities. One of them is a cloud data storage. Cloud Storage is a model of the data warehouse, which is located in the network on a large number of servers and provides non-stop customer access to their data from anywhere and any device with Internet access.
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Cappelli, Gino. "Data cloud through google cloud storage." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amslaurea.unibo.it/3032/.

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Il Cloud Storage è un modello di conservazione dati su computer in rete, dove i dati stessi sono memorizzati su molteplici server, reali e/o virtuali, generalmente ospitati presso strutture di terze parti o su server dedicati. Tramite questo modello è possibile accedere alle informazioni personali o aziendali, siano essi video, fotografie, musica, database o file in maniera “smaterializzata”, senza conoscere l’ubicazione fisica dei dati, da qualsiasi parte del mondo, con un qualsiasi dispositivo adeguato. I vantaggi di questa metodologia sono molteplici: infinita capacita’ di spazio di memoria, pagamento solo dell’effettiva quantità di memoria utilizzata, file accessibili da qualunque parte del mondo, manutenzione estremamente ridotta e maggiore sicurezza in quanto i file sono protetti da furto, fuoco o danni che potrebbero avvenire su computer locali. Google Cloud Storage cade in questa categoria: è un servizio per sviluppatori fornito da Google che permette di salvare e manipolare dati direttamente sull’infrastruttura di Google. In maggior dettaglio, Google Cloud Storage fornisce un’interfaccia di programmazione che fa uso di semplici richieste HTTP per eseguire operazioni sulla propria infrastruttura. Esempi di operazioni ammissibili sono: upload di un file, download di un file, eliminazione di un file, ottenere la lista dei file oppure la dimensione di un dato file. Ogniuna di queste richieste HTTP incapsula l’informazione sul metodo utilizzato (il tipo di richista, come GET, PUT, ...) e un’informazione di “portata” (la risorsa su cui effettuare la richiesta). Ne segue che diventa possibile la creazione di un’applicazione che, facendo uso di queste richieste HTTP, fornisce un servizio di Cloud Storage (in cui le applicazioni salvano dati in remoto generalmene attraverso dei server di terze parti). In questa tesi, dopo aver analizzato tutti i dettagli del servizio Google Cloud Storage, è stata implementata un’applicazione, chiamata iHD, che fa uso di quest’ultimo servizio per salvare, manipolare e condividere dati in remoto (nel “cloud”). Operazioni comuni di questa applicazione permettono di condividere cartelle tra più utenti iscritti al servizio, eseguire operazioni di upload e download di file, eliminare cartelle o file ed infine creare cartelle. L’esigenza di un’appliazione di questo tipo è nata da un forte incremento, sul merato della telefonia mobile, di dispositivi con tecnologie e con funzioni sempre più legate ad Internet ed alla connettività che esso offre. La tesi presenta anche una descrizione delle fasi di progettazione e implementazione riguardanti l’applicazione iHD. Nella fase di progettazione si sono analizzati tutti i requisiti funzionali e non funzionali dell’applicazione ed infine tutti i moduli da cui è composta quest’ultima. Infine, per quanto riguarda la fase di implementazione, la tesi presenta tutte le classi ed i rispettivi metodi presenti per ogni modulo, ed in alcuni casi anche come queste classi sono state effettivamente implementate nel linguaggio di programmazione utilizzato.
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Wang, Xing. "Benchmarking Cloud Storage Systems." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for telematikk, 2014. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-26716.

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With the rise of cloud computing, many cloud storage systems like Dropbox, Google Drive and Mega have been built to provide decentralized and reliable file storage. It is thus of prime importance to know their features, performance, and the best way to make use of them. In this context, we introduce BenchCloud, a tool designed as part of this thesis to conveniently and efficiently benchmark any cloud storage system.First, we provide a study of six commonly-used cloud storage systems to identify different types of their features. Then existing benchmarking tools for cloud systems are presented, and the requirements, design goals and internal architecture of BenchCloud are studied. Finally, we show how to use BenchCloud to analysis cloud storage systems and take a series of experiments on Dropbox to show how BenchCloud can be used to benchmark and inspect various kinds of features of cloud storage systems.
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Al, Beshri Aiiad Ahmad M. "Outsourcing data storage without outsourcing trust in cloud computing." Thesis, Queensland University of Technology, 2013. https://eprints.qut.edu.au/61738/1/Aiiad_Ahmad_M._Al_Beshri_Thesis.pdf.

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The main theme of this thesis is to allow the users of cloud services to outsource their data without the need to trust the cloud provider. The method is based on combining existing proof-of-storage schemes with distance-bounding protocols. Specifically, cloud customers will be able to verify the confidentiality, integrity, availability, fairness (or mutual non-repudiation), data freshness, geographic assurance and replication of their stored data directly, without having to rely on the word of the cloud provider.
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Bilbray, Kyle. "DSFS: a data storage facilitating service for maximizing security, availability, performance, and customizability." Thesis, Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/52984.

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The objective of this thesis is to study methods for the flexible and secure storage of sensitive data in an unaltered cloud. While current cloud storage providers make guarantees on the availability and security of data once it enters their domain, clients are not given any options for customization. All availability and security measures, along with any resulting performance hits, are applied to all requests, regardless of the data's sensitivity or client's wishes. In addition, once a client's data enters the cloud, it becomes vulnerable to different types of attacks. Other cloud users may access or disrupt the availability of their peers' data, and cloud providers cannot protect from themselves in the event of a malicious administrator or government directive. Current solutions use combinations of known encoding schemes and encryption techniques to provide confidentiality from peers and sometimes the cloud service provider, but its an all-or-nothing model. A client either uses the security methods of their system, or does not, regardless of whether the client's data needs more or less protection and availability. Our approach, referred to as the Data Storage Facilitating Service (DSFS), involves providing a basic set of proven protection schemes with configurable parameters that encode input data into a number of fragments and intelligently scatters them across the target cloud. A client may choose the encoding scheme most appropriate for the sensitivity of their data. If none of the supported schemes are sufficient for the client's needs or the client has their own custom encoding, DSFS can accept already encoded fragments and perform secure placement. Evaluation of our prototype service demonstrates clear trade-offs in performance between the different levels of security encoding provides, allowing clients to choose how much the importance of their data is worth. This amount of flexibility is unique to DSFS and turns it into more of a secure storage facilitator that can help clients as much or as little as required. We also see a significant effect on overhead from the service's location relative to its cloud when we compare performances of our own setup with a commercial cloud service.
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Gonçalves, André Miguel Augusto. "Estimating data divergence in cloud computing storage systems." Master's thesis, Faculdade de Ciências e Tecnologia, 2013. http://hdl.handle.net/10362/10852.

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Dissertação para obtenção do Grau de Mestre em Engenharia Informática
Many internet services are provided through cloud computing infrastructures that are composed of multiple data centers. To provide high availability and low latency, data is replicated in machines in different data centers, which introduces the complexity of guaranteeing that clients view data consistently. Data stores often opt for a relaxed approach to replication, guaranteeing only eventual consistency, since it improves latency of operations. However, this may lead to replicas having different values for the same data. One solution to control the divergence of data in eventually consistent systems is the usage of metrics that measure how stale data is for a replica. In the past, several algorithms have been proposed to estimate the value of these metrics in a deterministic way. An alternative solution is to rely on probabilistic metrics that estimate divergence with a certain degree of certainty. This relaxes the need to contact all replicas while still providing a relatively accurate measurement. In this work we designed and implemented a solution to estimate the divergence of data in eventually consistent data stores, that scale to many replicas by allowing clientside caching. Measuring the divergence when there is a large number of clients calls for the development of new algorithms that provide probabilistic guarantees. Additionally, unlike previous works, we intend to focus on measuring the divergence relative to a state that can lead to the violation of application invariants.
Partially funded by project PTDC/EIA EIA/108963/2008 and by an ERC Starting Grant, Agreement Number 307732
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7

Kaaniche, Nesrine. "Cloud data storage security based on cryptographic mechanisms." Thesis, Evry, Institut national des télécommunications, 2014. http://www.theses.fr/2014TELE0033/document.

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Au cours de la dernière décennie, avec la standardisation d’Internet, le développement des réseaux à haut débit, le paiement à l’usage et la quête sociétale de la mobilité, le monde informatique a vu se populariser un nouveau paradigme, le Cloud. Le recours au cloud est de plus en plus remarquable compte tenu de plusieurs facteurs, notamment ses architectures rentables, prenant en charge la transmission, le stockage et le calcul intensif de données. Cependant, ces services de stockage prometteurs soulèvent la question de la protection des données et de la conformité aux réglementations, considérablement due à la perte de maîtrise et de gouvernance. Cette dissertation vise à surmonter ce dilemme, tout en tenant compte de deux préoccupations de sécurité des données, à savoir la confidentialité des données et l’intégrité des données. En premier lieu, nous nous concentrons sur la confidentialité des données, un enjeu assez considérable étant donné le partage de données flexible au sein d’un groupe dynamique d’utilisateurs. Cet enjeu exige, par conséquence, un partage efficace des clés entre les membres du groupe. Pour répondre à cette préoccupation, nous avons, d’une part, proposé une nouvelle méthode reposant sur l’utilisation de la cryptographie basée sur l’identité (IBC), où chaque client agit comme une entité génératrice de clés privées. Ainsi, il génère ses propres éléments publics et s’en sert pour le calcul de sa clé privée correspondante. Grâce aux propriétés d’IBC, cette contribution a démontré sa résistance face aux accès non autorisés aux données au cours du processus de partage, tout en tenant compte de deux modèles de sécurité, à savoir un serveur de stockage honnête mais curieux et un utilisateur malveillant. D’autre part, nous définissons CloudaSec, une solution à base de clé publique, qui propose la séparation de la gestion des clés et les techniques de chiffrement, sur deux couches. En effet, CloudaSec permet un déploiement flexible d’un scénario de partage de données ainsi que des garanties de sécurité solides pour les données externalisées sur les serveurs du cloud. Les résultats expérimentaux, sous OpenStack Swift, ont prouvé l’efficacité de CloudaSec, en tenant compte de l’impact des opérations cryptographiques sur le terminal du client. En deuxième lieu, nous abordons la problématique de la preuve de possession de données (PDP). En fait, le client du cloud doit avoir un moyen efficace lui permettant d’effectuer des vérifications périodiques d’intégrité à distance, sans garder les données localement. La preuve de possession se base sur trois aspects : le niveau de sécurité, la vérification publique, et les performances. Cet enjeu est amplifié par des contraintes de stockage et de calcul du terminal client et de la taille des données externalisées. Afin de satisfaire à cette exigence de sécurité, nous définissons d’abord un nouveau protocole PDP, sans apport de connaissance, qui fournit des garanties déterministes de vérification d’intégrité, en s’appuyant sur l’unicité de la division euclidienne. Ces garanties sont considérées comme intéressantes par rapport à plusieurs schémas proposés, présentant des approches probabilistes. Ensuite, nous proposons SHoPS, un protocole de preuve de possession de données capable de traiter les trois relations d’ensembles homomorphiques. SHoPS permet ainsi au client non seulement d’obtenir une preuve de la possession du serveur distant, mais aussi de vérifier que le fichier, en question, est bien réparti sur plusieurs périphériques de stockage permettant d’atteindre un certain niveau de la tolérance aux pannes. En effet, nous présentons l’ensemble des propriétés homomorphiques, qui étend la malléabilité du procédé aux propriétés d’union, intersection et inclusion
Recent technological advances have given rise to the popularity and success of cloud. This new paradigm is gaining an expanding interest, since it provides cost efficient architectures that support the transmission, storage, and intensive computing of data. However, these promising storage services bring many challenging design issues, considerably due to the loss of data control. These challenges, namely data confidentiality and data integrity, have significant influence on the security and performances of the cloud system. This thesis aims at overcoming this trade-off, while considering two data security concerns. On one hand, we focus on data confidentiality preservation which becomes more complex with flexible data sharing among a dynamic group of users. It requires the secrecy of outsourced data and an efficient sharing of decrypting keys between different authorized users. For this purpose, we, first, proposed a new method relying on the use of ID-Based Cryptography (IBC), where each client acts as a Private Key Generator (PKG). That is, he generates his own public elements and derives his corresponding private key using a secret. Thanks to IBC properties, this contribution is shown to support data privacy and confidentiality, and to be resistant to unauthorized access to data during the sharing process, while considering two realistic threat models, namely an honest but curious server and a malicious user adversary. Second, we define CloudaSec, a public key based solution, which proposes the separation of subscription-based key management and confidentiality-oriented asymmetric encryption policies. That is, CloudaSec enables flexible and scalable deployment of the solution as well as strong security guarantees for outsourced data in cloud servers. Experimental results, under OpenStack Swift, have proven the efficiency of CloudaSec in scalable data sharing, while considering the impact of the cryptographic operations at the client side. On the other hand, we address the Proof of Data Possession (PDP) concern. In fact, the cloud customer should have an efficient way to perform periodical remote integrity verifications, without keeping the data locally, following three substantial aspects : security level, public verifiability, and performance. This concern is magnified by the client’s constrained storage and computation capabilities and the large size of outsourced data. In order to fulfill this security requirement, we first define a new zero-knowledge PDP proto- col that provides deterministic integrity verification guarantees, relying on the uniqueness of the Euclidean Division. These guarantees are considered as interesting, compared to several proposed schemes, presenting probabilistic approaches. Then, we propose SHoPS, a Set-Homomorphic Proof of Data Possession scheme, supporting the 3 levels of data verification. SHoPS enables the cloud client not only to obtain a proof of possession from the remote server, but also to verify that a given data file is distributed across multiple storage devices to achieve a certain desired level of fault tolerance. Indeed, we present the set homomorphism property, which extends malleability to set operations properties, such as union, intersection and inclusion. SHoPS presents high security level and low processing complexity. For instance, SHoPS saves energy within the cloud provider by distributing the computation over multiple nodes. Each node provides proofs of local data block sets. This is to make applicable, a resulting proof over sets of data blocks, satisfying several needs, such as, proofs aggregation
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8

Kaaniche, Nesrine. "Cloud data storage security based on cryptographic mechanisms." Electronic Thesis or Diss., Evry, Institut national des télécommunications, 2014. http://www.theses.fr/2014TELE0033.

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Au cours de la dernière décennie, avec la standardisation d’Internet, le développement des réseaux à haut débit, le paiement à l’usage et la quête sociétale de la mobilité, le monde informatique a vu se populariser un nouveau paradigme, le Cloud. Le recours au cloud est de plus en plus remarquable compte tenu de plusieurs facteurs, notamment ses architectures rentables, prenant en charge la transmission, le stockage et le calcul intensif de données. Cependant, ces services de stockage prometteurs soulèvent la question de la protection des données et de la conformité aux réglementations, considérablement due à la perte de maîtrise et de gouvernance. Cette dissertation vise à surmonter ce dilemme, tout en tenant compte de deux préoccupations de sécurité des données, à savoir la confidentialité des données et l’intégrité des données. En premier lieu, nous nous concentrons sur la confidentialité des données, un enjeu assez considérable étant donné le partage de données flexible au sein d’un groupe dynamique d’utilisateurs. Cet enjeu exige, par conséquence, un partage efficace des clés entre les membres du groupe. Pour répondre à cette préoccupation, nous avons, d’une part, proposé une nouvelle méthode reposant sur l’utilisation de la cryptographie basée sur l’identité (IBC), où chaque client agit comme une entité génératrice de clés privées. Ainsi, il génère ses propres éléments publics et s’en sert pour le calcul de sa clé privée correspondante. Grâce aux propriétés d’IBC, cette contribution a démontré sa résistance face aux accès non autorisés aux données au cours du processus de partage, tout en tenant compte de deux modèles de sécurité, à savoir un serveur de stockage honnête mais curieux et un utilisateur malveillant. D’autre part, nous définissons CloudaSec, une solution à base de clé publique, qui propose la séparation de la gestion des clés et les techniques de chiffrement, sur deux couches. En effet, CloudaSec permet un déploiement flexible d’un scénario de partage de données ainsi que des garanties de sécurité solides pour les données externalisées sur les serveurs du cloud. Les résultats expérimentaux, sous OpenStack Swift, ont prouvé l’efficacité de CloudaSec, en tenant compte de l’impact des opérations cryptographiques sur le terminal du client. En deuxième lieu, nous abordons la problématique de la preuve de possession de données (PDP). En fait, le client du cloud doit avoir un moyen efficace lui permettant d’effectuer des vérifications périodiques d’intégrité à distance, sans garder les données localement. La preuve de possession se base sur trois aspects : le niveau de sécurité, la vérification publique, et les performances. Cet enjeu est amplifié par des contraintes de stockage et de calcul du terminal client et de la taille des données externalisées. Afin de satisfaire à cette exigence de sécurité, nous définissons d’abord un nouveau protocole PDP, sans apport de connaissance, qui fournit des garanties déterministes de vérification d’intégrité, en s’appuyant sur l’unicité de la division euclidienne. Ces garanties sont considérées comme intéressantes par rapport à plusieurs schémas proposés, présentant des approches probabilistes. Ensuite, nous proposons SHoPS, un protocole de preuve de possession de données capable de traiter les trois relations d’ensembles homomorphiques. SHoPS permet ainsi au client non seulement d’obtenir une preuve de la possession du serveur distant, mais aussi de vérifier que le fichier, en question, est bien réparti sur plusieurs périphériques de stockage permettant d’atteindre un certain niveau de la tolérance aux pannes. En effet, nous présentons l’ensemble des propriétés homomorphiques, qui étend la malléabilité du procédé aux propriétés d’union, intersection et inclusion
Recent technological advances have given rise to the popularity and success of cloud. This new paradigm is gaining an expanding interest, since it provides cost efficient architectures that support the transmission, storage, and intensive computing of data. However, these promising storage services bring many challenging design issues, considerably due to the loss of data control. These challenges, namely data confidentiality and data integrity, have significant influence on the security and performances of the cloud system. This thesis aims at overcoming this trade-off, while considering two data security concerns. On one hand, we focus on data confidentiality preservation which becomes more complex with flexible data sharing among a dynamic group of users. It requires the secrecy of outsourced data and an efficient sharing of decrypting keys between different authorized users. For this purpose, we, first, proposed a new method relying on the use of ID-Based Cryptography (IBC), where each client acts as a Private Key Generator (PKG). That is, he generates his own public elements and derives his corresponding private key using a secret. Thanks to IBC properties, this contribution is shown to support data privacy and confidentiality, and to be resistant to unauthorized access to data during the sharing process, while considering two realistic threat models, namely an honest but curious server and a malicious user adversary. Second, we define CloudaSec, a public key based solution, which proposes the separation of subscription-based key management and confidentiality-oriented asymmetric encryption policies. That is, CloudaSec enables flexible and scalable deployment of the solution as well as strong security guarantees for outsourced data in cloud servers. Experimental results, under OpenStack Swift, have proven the efficiency of CloudaSec in scalable data sharing, while considering the impact of the cryptographic operations at the client side. On the other hand, we address the Proof of Data Possession (PDP) concern. In fact, the cloud customer should have an efficient way to perform periodical remote integrity verifications, without keeping the data locally, following three substantial aspects : security level, public verifiability, and performance. This concern is magnified by the client’s constrained storage and computation capabilities and the large size of outsourced data. In order to fulfill this security requirement, we first define a new zero-knowledge PDP proto- col that provides deterministic integrity verification guarantees, relying on the uniqueness of the Euclidean Division. These guarantees are considered as interesting, compared to several proposed schemes, presenting probabilistic approaches. Then, we propose SHoPS, a Set-Homomorphic Proof of Data Possession scheme, supporting the 3 levels of data verification. SHoPS enables the cloud client not only to obtain a proof of possession from the remote server, but also to verify that a given data file is distributed across multiple storage devices to achieve a certain desired level of fault tolerance. Indeed, we present the set homomorphism property, which extends malleability to set operations properties, such as union, intersection and inclusion. SHoPS presents high security level and low processing complexity. For instance, SHoPS saves energy within the cloud provider by distributing the computation over multiple nodes. Each node provides proofs of local data block sets. This is to make applicable, a resulting proof over sets of data blocks, satisfying several needs, such as, proofs aggregation
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9

Noman, Ali. "Addressing the Data Location Assurance Problem of Cloud Storage Environments." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/37375.

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In a cloud storage environment, providing geo-location assurance of data to a cloud user is very challenging as the cloud storage provider physically controls the data and it would be challenging for the user to detect if the data is stored in different datacenters/storage servers other than the one where it is supposed to be. We name this problem as the “Data Location Assurance Problem” of a Cloud Storage Environment. Aside from the privacy and security concerns, the lack of geo-location assurance of cloud data involved in the cloud storage has been identified as one of the main reasons why organizations that deal with sensitive data (e.g., financial data, health-related data, and data related to Personally Identifiable Infor-mation, PII) cannot adopt a cloud storage solution even if they might wish to. It might seem that cryptographic techniques such as Proof of Data Possession (PDP) can be a solution for this problem; however, we show that those cryptographic techniques alone cannot solve that. In this thesis, we address the data location assurance (DLA) problem of the cloud storage environment which includes but is not limited to investigating the necessity for a good data location assurance solution as well as challenges involved in providing this kind of solution; we then come up with efficient solutions for the DLA problem. Note that, for the totally dis-honest cloud storage server attack model, it may be impossible to offer a solution for the DLA problem. So the main objective of this thesis is to come up with solutions for the DLA problem for different system and attack models (from less adversarial system and attack models to more adversarial ones) available in existing cloud storage environments so that it can meet the need for cloud storage applications that exist today.
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Arteaga, Clavijo Dulcardo Ariel. "Flash Caching for Cloud Computing Systems." FIU Digital Commons, 2016. http://digitalcommons.fiu.edu/etd/2496.

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As the size of cloud systems and the number of hosted virtual machines (VMs) rapidly grow, the scalability of shared VM storage systems becomes a serious issue. Client-side flash-based caching has the potential to improve the performance of cloud VM storage by employing flash storage available on the VM hosts to exploit the locality inherent in VM IOs. However, there are several challenges to the effective use of flash caching in cloud systems. First, cache configurations such as size, write policy, metadata persistency and RAID level have significant impacts on flash caching. Second, the typical capacity of flash devices is limited compared to the dataset size of consolidated VMs. Finally, flash devices wear out and face serious endurance issues which are aggravated by the use for caching. This dissertation presents the research for addressing these problems of cloud flash caching in the following three aspects. First, it presents a thorough study of different cache configurations including a new cache-optimized RAID configuration using a large amount of long-term traces collected from real-world public and private clouds. Second, it studies an on-demand flash cache management solution for meeting VM cache demands and minimizing device wear-out. It uses a new cache demand model Reuse Working Set (RWS) to capture the data with good temporal locality, and uses the RWS size (RWSS) to model a workload?s cache demand. Finally, to handle situations where a cache is insufficient for VMs? demands, it employs dynamic cache migration to balance cache load across hosts by live migrating cached data along with the VMs. The results show that the cache-optimized RAID improves performance by 137% without sacrificing reliability, compared to traditional RAID. The RWSS-based on-demand cache allocation reduces workload?s cache usage by 78% and lowers the amount of writes sent to cache device by 40%, compared to traditional working set based cache allocation. Combining on-demand cache allocation with dynamic cache migration for 12 concurrent VMs, results show 28% higher hit ratio and 28% lower 90th percentile IO latency, compared to the case without cache allocation.
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Books on the topic "Cloud data storage"

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Zhang, Yuan, Chunxiang Xu, and Xuemin Sherman Shen. Data Security in Cloud Storage. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4374-6.

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Cloud and virtual data storage networking. Boca Raton, FL: CRC Press, 2012.

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Data intensive storage services for cloud environments. Hershey, PA: Business Science Reference, 2013.

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Deshpande, Prachi S., Subhash C. Sharma, and Sateesh K. Peddoju. Security and Data Storage Aspect in Cloud Computing. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6089-3.

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Making Data Storage Efficient in the Era of Cloud Computing. [New York, N.Y.?]: [publisher not identified], 2020.

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International Business Machines Corporation. International Technical Support Organization, ed. Managing security and compliance in cloud or virtualized data centers. [Poughkeepsie, NY: IBM Corp., International Technical Support Organization], 2013.

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Hill, Richard. Guide to Cloud Computing: Principles and Practice. London: Springer London, 2013.

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Grid and cloud database management. Heidelberg: Springer, 2011.

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author, Ward D. Dewey, Latham, Claire Kamm, 1953- author, and Copeland Mary Kathleen author, eds. Computerized accounting in the cloud using Microsoft Dynamics GP 2013. 7th ed. Okemos, Michigan: Armond Dalton Publishers, Inc., 2014.

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M, Butler Joe, Theilmann Wolfgang, Yahyapour Ramin, and SpringerLink (Online service), eds. Service Level Agreements for Cloud Computing. New York, NY: Springer Science+Business Media, LLC, 2011.

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Book chapters on the topic "Cloud data storage"

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Frampton, Michael. "Cloud Storage." In Complete Guide to Open Source Big Data Stack, 17–58. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-2149-5_2.

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Zhang, Yuan, Chunxiang Xu, and Xuemin Sherman Shen. "Cloud Storage Reliability." In Data Security in Cloud Storage, 29–54. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4374-6_3.

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Kamara, Seny, and Kristin Lauter. "Cryptographic Cloud Storage." In Financial Cryptography and Data Security, 136–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14992-4_13.

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Zhao, Liang, Sherif Sakr, Anna Liu, and Athman Bouguettaya. "Cloud-Hosted Data Storage Systems." In Cloud Data Management, 21–45. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04765-2_3.

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Kaoudi, Zoi, Ioana Manolescu, and Stamatis Zampetakis. "Cloud-Based RDF Storage." In Cloud-Based RDF Data Management, 21–42. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-031-01875-6_3.

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Zhang, Yuan, Chunxiang Xu, and Xuemin Sherman Shen. "Secure Data Provenance." In Data Security in Cloud Storage, 119–41. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4374-6_6.

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Chen, Yu, Wei-Shinn Ku, Jun Feng, Pu Liu, and Zhou Su. "Secure Distributed Data Storage in Cloud Computing." In Cloud Computing, 221–48. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9780470940105.ch8.

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Sukhdeve, Shitalkumar R., and Sandika S. Sukhdeve. "Data Analytics and Storage." In Google Cloud Platform for Data Science, 161–87. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9688-2_6.

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Zhang, Yuan, Chunxiang Xu, and Xuemin Sherman Shen. "Secure Data Time-Stamping." In Data Security in Cloud Storage, 143–66. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4374-6_7.

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Shashi, Ashutosh. "Data Storage in Google Cloud." In Designing Applications for Google Cloud Platform, 73–117. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9511-3_4.

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Conference papers on the topic "Cloud data storage"

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Matveev, Artem. "Cost-Efficient Data Privacy Protection in Multi Cloud Storage." In 3rd International Conference on Data Mining and Machine Learning (DMML 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120706.

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Data privacy in the cloud is a big concern for all of its users, especially for public clouds. Modern trends in studies utilise multiple clouds to achieve data privacy protection. Most of the present studies focus on business-oriented solutions, but current study aims to create a solution for individual users which would not increase the cost of ownership, and provide enough flexibility and privacy protection by combining password protection, key-derivation, multilayer encryption and key distribution across multiple clouds. New design allows to use single cloud to store protected user data, meanwhile use free plans on other clouds to store key information on others and thereby does not rise a cost of the solution. As a result, proposed design gives multiple layers of protection of Data Privacy while having a low cost of use. With some further adaptation it could be proposed as a business solution.
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Baalagi, R., H. Sindhura, M. Keerthi, and Golda Dilip. "Optimizing Information Leakage in Multi Cloud Storage Services." In International Research Conference on IOT, Cloud and Data Science. Switzerland: Trans Tech Publications Ltd, 2023. http://dx.doi.org/10.4028/p-mv0271.

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Many new strategies for storing data across different clouds have lately been proposed. Because a single point of attack can’t leak all the information, distributing data among multiple cloud storage providers (CSP) automatically gives users some control over information leaking. However, if data chunks are dispersed in an unstructured way over different clouds, there will be a lot of information leakage. To prevent data loss due to hacking or server failure, the data is uploaded to different servers. To access owners' data, secure files, and prevent data leakage, we employ advanced techniques such as Advanced Encryption Standard (AES) and Fully Homomorphic Encryption (FHE).
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Li, Chao, and Balaji Palanisamy. "Emerge: Self-Emerging Data Release Using Cloud Data Storage." In 2017 IEEE 10th International Conference on Cloud Computing (CLOUD). IEEE, 2017. http://dx.doi.org/10.1109/cloud.2017.13.

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Vernik, Gil, Alexandra Shulman-Peleg, Sebastian Dippl, Ciro Formisano, Michael C. Jaeger, Elliot K. Kolodner, and Massimo Villari. "Data On-Boarding in Federated Storage Clouds." In 2013 IEEE 6th International Conference on Cloud Computing (CLOUD). IEEE, 2013. http://dx.doi.org/10.1109/cloud.2013.54.

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Kaaniche, Nesrine, Aymen Boudguiga, and Maryline Laurent. "ID Based Cryptography for Cloud Data Storage." In 2013 IEEE 6th International Conference on Cloud Computing (CLOUD). IEEE, 2013. http://dx.doi.org/10.1109/cloud.2013.80.

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Parwekar, Pritee, Prakash Kumar, Mayuri Saxena, and Sakshi Saxena. "Public auditing: Cloud data storage." In 2014 5th International Conference- Confluence The Next Generation Information Technology Summit. IEEE, 2014. http://dx.doi.org/10.1109/confluence.2014.6949366.

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Dongre, Kirti A., Roshan Singh Thakur, and Allan Abraham. "Secure cloud storage of data." In 2014 International Conference on Computer Communication and Informatics (ICCCI). IEEE, 2014. http://dx.doi.org/10.1109/iccci.2014.6921741.

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Kurra, Hemayamini, Youssif Al-Nashif, and Salim Hariri. "Resilient cloud data storage services." In the 2013 ACM Cloud and Autonomic Computing Conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2494621.2494634.

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Jordao, Renata, Valerio Aymore Martins, Fabio Buiati, Rafael Timoteo de Sousa, and Flavio Elias de Deus. "Secure data storage in distributed cloud environments." In 2014 IEEE International Conference on Big Data (Big Data). IEEE, 2014. http://dx.doi.org/10.1109/bigdata.2014.7004383.

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Yunqi Ye, Liangliang Xiao, Yinzi Chen, I-Ling Yen, Farokh Bastani, and Ing-Ray Chen. "Access Protocols in Data Partitioning Based Cloud Storage." In 2013 IEEE 6th International Conference on Cloud Computing (CLOUD). IEEE, 2013. http://dx.doi.org/10.1109/cloud.2013.23.

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Reports on the topic "Cloud data storage"

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Semerikov, Serhiy, Illia Teplytskyi, Yuliia Yechkalo, Oksana Markova, Vladimir Soloviev, and Arnold Kiv. Computer Simulation of Neural Networks Using Spreadsheets: Dr. Anderson, Welcome Back. [б. в.], June 2019. http://dx.doi.org/10.31812/123456789/3178.

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The authors of the given article continue the series presented by the 2018 paper “Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot”. This time, they consider mathematical informatics as the basis of higher engineering education fundamentalization. Mathematical informatics deals with smart simulation, information security, long-term data storage and big data management, artificial intelligence systems, etc. The authors suggest studying basic principles of mathematical informatics by applying cloud-oriented means of various levels including those traditionally considered supplementary – spreadsheets. The article considers ways of building neural network models in cloud-oriented spreadsheets, Google Sheets. The model is based on the problem of classifying multi-dimensional data provided in “The Use of Multiple Measurements in Taxonomic Problems” by R. A. Fisher. Edgar Anderson’s role in collecting and preparing the data in the 1920s-1930s is discussed as well as some peculiarities of data selection. There are presented data on the method of multi-dimensional data presentation in the form of an ideograph developed by Anderson and considered one of the first efficient ways of data visualization.
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Kong, Zhihao, and Na Lu. Determining Optimal Traffic Opening Time Through Concrete Strength Monitoring: Wireless Sensing. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317613.

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Construction and concrete production are time-sensitive and fast-paced; as such, it is crucial to monitor the in-place strength development of concrete structures in real-time. Existing concrete strength testing methods, such as the traditional hydraulic compression method specified by ASTM C 39 and the maturity method specified by ASTM C 1074, are labor-intensive, time consuming, and difficult to implement in the field. INDOT’s previous research (SPR-4210) on the electromechanical impedance (EMI) technique has established its feasibility for monitoring in-situ concrete strength to determine the optimal traffic opening time. However, limitations of the data acquisition and communication systems have significantly hindered the technology’s adoption for practical applications. Furthermore, the packaging of piezoelectric sensor needs to be improved to enable robust performance and better signal quality. In this project, a wireless concrete sensor with a data transmission system was developed. It was comprised of an innovated EMI sensor and miniaturized datalogger with both wireless transmission and USB module. A cloud-based platform for data storage and computation was established, which provides the real time data visualization access to general users and data access to machine learning and data mining developers. Furthermore, field implementations were performed to prove the functionality of the innovated EMI sensor and wireless sensing system for real-time and in-place concrete strength monitoring. This project will benefit the DOTs in areas like construction, operation, and maintenance scheduling and asset management by delivering applicable concrete strength monitoring solutions.
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Rudd, Ian. Leveraging Artificial Intelligence and Robotics to Improve Mental Health. Intellectual Archive, July 2022. http://dx.doi.org/10.32370/iaj.2710.

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Artificial Intelligence (AI) is one of the oldest fields of computer science used in building structures that look like human beings in terms of thinking, learning, solving problems, and decision making (Jovanovic et al., 2021). AI technologies and techniques have been in application in various aspects to aid in solving problems and performing tasks more reliably, efficiently, and effectively than what would happen without their use. These technologies have also been reshaping the health sector's field, particularly digital tools and medical robotics (Dantas & Nogaroli, 2021). The new reality has been feasible since there has been exponential growth in the patient health data collected globally. The different technological approaches are revolutionizing medical sciences into dataintensive sciences (Dantas & Nogaroli, 2021). Notably, with digitizing medical records supported the increasing cloud storage, the health sector created a vast and potentially immeasurable volume of biomedical data necessary for implementing robotics and AI. Despite the notable use of AI in healthcare sectors such as dermatology and radiology, its use in psychological healthcare has neem models. Considering the increased mortality and morbidity levels among patients with psychiatric illnesses and the debilitating shortage of psychological healthcare workers, there is a vital requirement for AI and robotics to help in identifying high-risk persons and providing measures that avert and treat mental disorders (Lee et al., 2021). This discussion is focused on understanding how AI and robotics could be employed in improving mental health in the human community. The continued success of this technology in other healthcare fields demonstrates that it could also be used in redefining mental sicknesses objectively, identifying them at a prodromal phase, personalizing the treatments, and empowering patients in their care programs.
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Zhylenko, Tetyana I. Auto Checker of Higher Mathematics - an element of mobile cloud education. [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3895.

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We analyzed the main cloud services in the article. We also described the main contribution of mobile cloud technology to education. The article presents the author’s development from the field of mobile cloud education in higher mathematics. The design architecture of this application is described in detail: QR generator and scanner, authorization, sending tasks. Block diagrams and images are presented that clearly demonstrate the operation of the application. We showed an example of solving the integral from the section of integral calculus for higher mathematics and showed how to download the answer in the form of a QR code and find out whether it is correct or incorrect (this can be seen by the color on the smart phone screen). It is shown how this technology helps the teacher save time for checking assignments completed by students. This confirms its effectiveness. Such an application provides students and teachers with the ability to store and process data on a cloud computing platform.
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