100 research outputs found
Survey on Secure Authorized De-duplication in Hybrid
Nowadays, cloud computing provides high amount of storage space and massive parallel computing at effective cost. As cloud computing becomes prevalent, excessive amount of data being stored in the cloud. However, exponential growth of ever-increasing volume of data has raised many new challenges. De-duplication technique is specialized data compression technique which eliminates redundant data as well as improves storage and bandwidth utilization. Convergent encryption technique is proposed to enforce confidentiality during de-duplication, which encrypt data before outsourcing. To better protect data security, we present different privileges of user to address problem of authorized data de-duplication. We also present several new de-duplication constructions supporting authorized duplicate check in hybrid cloud architecture, which incurs minimal overhead compared to normal operation
DDEAS: Distributed Deduplication System with Efficient Access in Cloud Data Storage
Cloud storage service is one of the vital function of
cloud computing that helps cloud users to outsource a massive
volume of data without upgrading their devices. However, cloud
data storage offered by Cloud Service Providers (CSPs) faces data
redundancy problems. The data de-duplication technique aims to
eliminate redundant data segments and keeps a single instance of
the data set, even if similar data set is owned by any number of
users. Since data blocks are distributed among the multiple
individual servers, the user needs to download each block of the
file before reconstructing the file, which reduces the system
efficiency. We propose a server level data recover module in the
cloud storage system to improve file access efficiency and reduce
network bandwidth utilization time. In the proposed method,
erasure coding is used to store blocks in distributed cloud storage
and The MD5 (Message Digest 5) is used for data integrity.
Executing recover algorithm helps user to directly fetch the file
without downloading each block from the cloud servers. The
proposed scheme improves the time efficiency of the system and
quick access ability to the stored data. Thus consumes less
network bandwidth and
Towards Data Optimization in Storages and Networks
Title from PDF of title page, viewed on August 7, 2015Dissertation advisors: Sejun Song and Baek-Young ChoiVitaIncludes bibliographic references (pages 132-140)Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2015We are encountering an explosion of data volume, as a study estimates that data
will amount to 40 zeta bytes by the end of 2020. This data explosion poses significant
burden not only on data storage space but also access latency, manageability, and processing
and network bandwidth. However, large portions of the huge data volume contain
massive redundancies that are created by users, applications, systems, and communication
models. Deduplication is a technique to reduce data volume by removing redundancies.
Reliability will be even improved when data is replicated after deduplication.
Many deduplication studies such as storage data deduplication and network redundancy
elimination have been proposed to reduce storage consumption and network
bandwidth consumption. However, existing solutions are not efficient enough to optimize
data delivery path from clients to servers through network. Hence we propose a holistic
deduplication framework to optimize data in their path. Our deduplication framework
consists of three components including data sources or clients, networks, and servers. The
client component removes local redundancies in clients, the network component removes
redundant transfers coming from different clients, and the server component removes redundancies
coming from different networks.
We designed and developed components for the proposed deduplication framework.
For the server component, we developed the Hybrid Email Deduplication System
that achieves a trade-off of space savings and overhead for email systems. For the client
component, we developed the Structure Aware File and Email Deduplication for Cloudbased
Storage Systems that is very fast as well as having good space savings by using
structure-based granularity. For the network component, we developed a system called
Software-defined Deduplication as a Network and Storage service that is in-network deduplication,
and that chains storage data deduplication and network redundancy elimination
functions by using Software Defined Network to achieve both storage space and network
bandwidth savings with low processing time and memory size. We also discuss mobile
deduplication for image and video files in mobile devices. Through system implementations
and experiments, we show that the proposed framework effectively and efficiently
optimizes data volume in a holistic manner encompassing the entire data path of clients,
networks and storage servers.Introduction -- Deduplication technology -- Existing deduplication approaches -- HEDS: Hybrid Email Deduplication System -- SAFE: Structure-aware File and Email Deduplication for cloud-based storage systems -- SoftDance: Software-defined Deduplication as a Network and Storage Service -- Moblie de-duplication -- Conclusion
The Family of MapReduce and Large Scale Data Processing Systems
In the last two decades, the continuous increase of computational power has
produced an overwhelming flow of data which has called for a paradigm shift in
the computing architecture and large scale data processing mechanisms.
MapReduce is a simple and powerful programming model that enables easy
development of scalable parallel applications to process vast amounts of data
on large clusters of commodity machines. It isolates the application from the
details of running a distributed program such as issues on data distribution,
scheduling and fault tolerance. However, the original implementation of the
MapReduce framework had some limitations that have been tackled by many
research efforts in several followup works after its introduction. This article
provides a comprehensive survey for a family of approaches and mechanisms of
large scale data processing mechanisms that have been implemented based on the
original idea of the MapReduce framework and are currently gaining a lot of
momentum in both research and industrial communities. We also cover a set of
introduced systems that have been implemented to provide declarative
programming interfaces on top of the MapReduce framework. In addition, we
review several large scale data processing systems that resemble some of the
ideas of the MapReduce framework for different purposes and application
scenarios. Finally, we discuss some of the future research directions for
implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author
A systematic review on cloud storage mechanisms concerning e-healthcare systems
As the expenses of medical care administrations rise and medical services experts are becoming rare, it is up to medical services organizations and institutes to consider the implementation of medical Health Information Technology (HIT) innovation frameworks. HIT permits health associations to smooth out their considerable cycles and offer types of assistance in a more productive and financially savvy way. With the rise of Cloud Storage Computing (CSC), an enormous number of associations and undertakings have moved their healthcare data sources to distributed storage. As the information can be mentioned whenever universally, the accessibility of information becomes an urgent need. Nonetheless, outages in cloud storage essentially influence the accessibility level. Like the other basic variables of cloud storage (e.g., reliability quality, performance, security, and protection), availability also directly impacts the data in cloud storage for e-Healthcare systems. In this paper, we systematically review cloud storage mechanisms concerning the healthcare environment. Additionally, in this paper, the state-of-the-art cloud storage mechanisms are critically reviewed for e-Healthcare systems based on their characteristics. In short, this paper summarizes existing literature based on cloud storage and its impact on healthcare, and it likewise helps researchers, medical specialists, and organizations with a solid foundation for future studies in the healthcare environment.Qatar University [IRCC-2020-009]
Chapter A Techno Social Collaborative Platform to Manage Optimize and Crowdfund Cultural Heritage Initiatives
This paper presents the first results of HERIBITS, a research project co-funded by the Tuscany Region Government, under the Operative Regional Program FESR POR 2014-2020. The project proposes an innovative methodology as well as advanced technology platform enabling a new paradigm for bottom-up and top-down management of cultural heritage initiatives. The techno-social platform integrates collaborative tools for CrowdSourcing, as well as analysis tools for rating project ideas and evaluate socio-economic impact, to propose best practices and to detect similar initiatives in order to avoid project duplications. The platform provides also social network capabilities and integrates an ad-hoc CrowdFunding shop
Big data reduction framework for value creation in sustainable enterprises
Value creation is a major sustainability factor for enterprises, in addition to profit maximization and revenue generation. Modern enterprises collect big data from various inbound and outbound data sources. The inbound data sources handle data generated from the results of business operations, such as manufacturing, supply chain management, marketing, and human resource management, among others. Outbound data sources handle customer-generated data which are acquired directly or indirectly from customers, market analysis, surveys, product reviews, and transactional histories. However, cloud service utilization costs increase because of big data analytics and value creation activities for enterprises and customers. This article presents a novel concept of big data reduction at the customer end in which early data reduction operations are performed to achieve multiple objectives, such as a) lowering the service utilization cost, b) enhancing the trust between customers and enterprises, c) preserving privacy of customers, d) enabling secure data sharing, and e) delegating data sharing control to customers. We also propose a framework for early data reduction at customer end and present a business model for end-to-end data reduction in enterprise applications. The article further presents a business model canvas and maps the future application areas with its nine components. Finally, the article discusses the technology adoption challenges for value creation through big data reduction in enterprise applications
Secure data sharing in cloud computing: a comprehensive review
Cloud Computing is an emerging technology, which relies on sharing computing resources. Sharing of data in the group is not secure as the cloud provider cannot be trusted. The fundamental difficulties in distributed computing of cloud suppliers is Data Security, Sharing, Resource scheduling and Energy consumption. Key-Aggregate cryptosystem used to secure private/public data in the cloud. This key is consistent size aggregate for adaptable decisions of ciphertext in cloud storage. Virtual Machines (VMs) provisioning is effectively empowered the cloud suppliers to effectively use their accessible resources and get higher benefits. The most effective method to share information resources among the individuals from the group in distributed storage is secure, flexible and efficient. Any data stored in different cloud data centers are corrupted, recovery using regenerative coding. Security is provided many techniques like Forward security, backward security, Key-Aggregate cryptosystem, Encryption and Re-encryption etc. The energy is reduced using Energy-Efficient Virtual Machines Scheduling in Multi-Tenant Data Centers
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