Charon: A Secure Cloud-of-Clouds System for Storing and Sharing Big Data

Mendes, Ricardo; Oliveira, Tiago; Cogo, Vinicius Vielmo; Neves, Nuno; Bessani, Alysson

doi:10.1109/tcc.2019.2916856

Cited by 26 publications

(13 citation statements)

References 35 publications

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“…For several entities, private infrastructures are the first option. Creating and managing data centers, on the other hand, are costly, necessitate skilled employees and can (Mendes et al, 2019). The cloud is a storage solution that can consistently store and deliver large volumes of heterogeneous data (Fazio et al, 2015).…”

Section: Storingmentioning

confidence: 99%

How does cloud computing help businesses to manage big data issues

Latifian

2022

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PurposeBig data has posed problems for businesses, the Information Technology (IT) sector and the science community. The problems posed by big data can be effectively addressed using cloud computing and associated distributed computing technology. Cloud computing and big data are two significant past-year problems that allow high-efficiency and competitive computing tools to be delivered as IT services. The paper aims to examine the role of the cloud as a tool for managing big data in various aspects to help businesses.Design/methodology/approachThis paper delivers solutions in the cloud for storing, compressing, analyzing and processing big data. Hence, articles were divided into four categories: articles on big data storage, articles on big data processing, articles on analyzing and finally, articles on data compression in cloud computing. This article is based on a systematic literature review. Also, it is based on a review of 19 published papers on big data.FindingsFrom the results, it can be inferred that cloud computing technology has features that can be useful for big data management. Challenging issues are raised in each section. For example, in storing big data, privacy and security issues are challenging.Research limitations/implicationsThere were limitations to this systematic review. The first limitation is that only English articles were reviewed. Also, articles that matched the keywords were used. Finally, in this review, authoritative articles were reviewed, and slides and tutorials were avoided.Practical implicationsThe research presents new insight into the business value of cloud computing in interfirm collaborations.Originality/valuePrevious research has often examined other aspects of big data in the cloud. This article takes a new approach to the subject. It allows big data researchers to comprehend the various aspects of big data management in the cloud. In addition, setting an agenda for future research saves time and effort for readers searching for topics within big data.

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Section: Storingmentioning

confidence: 99%

How does cloud computing help businesses to manage big data issues

Latifian

2022

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“…More recent solutions, such as RockFS [ 21 ], have client-side protection against specific attacks, namely protection against ransomware, protection of the credential stored locally, or attacker access files on the local file system. Lastly, Charon [ 22 ] from the same research group of Depsky and SCFS , has support for big data processing and usage of BFT for coordination, such as the remaining solutions. All of these solutions can be considered an evolution of the previous solutions.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Similar systems do not address this issue, as they assume a client that performs uploads to and management of the cloud [ 22 ], or that the server is trusted and not vulnerable to impersonation attacks.…”

Section: Architecture Overviewmentioning

confidence: 99%

Towards a Modular On-Premise Approach for Data Sharing

Resende

Magalhães

Brandão

et al. 2021

Sensors

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The growing demand for everyday data insights drives the pursuit of more sophisticated infrastructures and artificial intelligence algorithms. When combined with the growing number of interconnected devices, this originates concerns about scalability and privacy. The main problem is that devices can detect the environment and generate large volumes of possibly identifiable data. Public cloud-based technologies have been proposed as a solution, due to their high availability and low entry costs. However, there are growing concerns regarding data privacy, especially with the introduction of the new General Data Protection Regulation, due to the inherent lack of control caused by using off-premise computational resources on which public cloud belongs. Users have no control over the data uploaded to such services as the cloud, which increases the uncontrolled distribution of information to third parties. This work aims to provide a modular approach that uses cloud-of-clouds to store persistent data and reduce upfront costs while allowing information to remain private and under users’ control. In addition to storage, this work also extends focus on usability modules that enable data sharing. Any user can securely share and analyze/compute the uploaded data using private computing without revealing private data. This private computation can be training machine learning (ML) models. To achieve this, we use a combination of state-of-the-art technologies, such as MultiParty Computation (MPC) and K-anonymization to produce a complete system with intrinsic privacy properties.

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“…CHARON [171] is a multi-cloud storage solution built for big data that exposes a near POSIX interface built for Byzantine fault tolerance. Relying on the constructs proposed by DepSky [21], CHARON stands out from other solutions by relying solely on publicly available cloud services, both for data, metadata and coordination.…”

Section: Secure Multi-cloud Storagementioning

confidence: 99%

“…In a multi-cloud deployment context, data is stored across multiple storage providers. This scenario brings benefits on several points: performance, storage capacity, data availability and security [305,21,67,257,171]. Under this scenario, we must guarantee that a single corrupted cloud provider does not lead to a full disclosure of the stored data.…”

Section: Multi-cloud Deploymentmentioning

confidence: 99%

Erasure coding for distributed storage systems

Burihabwa¹

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Cloud storage has durably entered the stage as go-to solution for business and personal storage. Virtually extending storage capabilities to infinity, cloud storage enables companies and individuals to focus on content creation without fear of running out of space or losing data. But as users entrust more and more data to the cloud, they also have to accept a loss of control over the data they o˜oad to the cloud. At a time when online services seem to be making a significant part of their profits by exploiting customer data, concerns over privacy and integrity of said data naturally arise. Are their online documents read by the storage provider or its employees? Is the content of these documents shared with third party partners of the storage provider? What happens if the provider goes bankrupt? Whatever answer can be o˙ered by the storage provider, the loss of control should be cause for concern. But storage providers also have to worry about trust and reliability. As they build distributed solutions to accommodate their customers’ needs, these concerns of control extend to the infrastructure they operate on. Conciliating security, confidentiality, resilience and perform-ance over large sets of distributed storage nodes is a tricky balancing act. And even when a suitable balance can be found, it is often done at the expense of increased storage overhead. In this dissertation, we try to mitigate these issues by focusing on three aspects. First, we study solutions to empower users with flexible tooling ensuring security, integrity and redundancy in distributed storage settings. By leveraging public cloud storage o˙erings to build a configurable file system and storage middleware, we show that securing cloud-storage from the client-side is an e˙ective way maintaining control. Second, we build a distributed archive whose resilience goes beyond standard redundancy schemes. To achieve this, we implement Recast, relying on a data entanglement scheme, that encodes and distributes data over a set of storage nodes to ensure durability at a manageable cost. Finally, we look into o˙setting the increase in storage overhead by means of data reduction. This is made possible by the use of Generalised Deduplication, a scheme that improves over classical data deduplication by detecting similarities beyond exact matches.

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Charon: A Secure Cloud-of-Clouds System for Storing and Sharing Big Data

Cited by 26 publications

References 35 publications

How does cloud computing help businesses to manage big data issues

How does cloud computing help businesses to manage big data issues

Towards a Modular On-Premise Approach for Data Sharing

Erasure coding for distributed storage systems

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