Secure Access control Technology towards Data Sharing and Storage in Cloud Computing

Cloud computing is a type of appropriated computing wherein assets and application stages are disseminated over the Internet through on request and pay on use premise. Many cloud storage encryption schemes have been acquainted with shield data from the individuals who don't approach. We make utilization of many schemes which accepted that cloud storage providers are protected and secure. Be that as it may, by and by, a few specialists (i.e., coercers) may attempt to uncover data from the cloud without the authorization of the data proprietor. In this paper, we exhibit that the location of obscurity clients with the utilization of our productive deniable encryption conspire, while the phony clients tries to get data from the cloud they will be furnished with some phony files. With the goal that programmers can't hack the files from the cloud. Also, they are happy with their copy record by that way we can secure the proprietor mystery files or confidential files

Impediments to Enterprise System Implementation across the System Lifecycle: Insights from Polish Practitioners

This study’s goal is to investigate impediments to successful enterprise system (ES) implementation across the system lifecycle. Drawing from the opinions of 82 ES practitioners and building on the authors’ previous work on source problems in ES adoption, this study performs the further data analysis incorporating the ES lifecycle. The analysis employs the Cooper and Zmud’s six-stage model of IT diffusion and investigates how the difficulties change along the ES lifecycle. Our findings suggest that Adaptation phase, which is the main implementation stage, is the most challenging period of the ES adoption project. The results also indicate that problems with employees are the most significant impediments to ES adoption success. The findings imply that difficulties during later stages of the ES adoption can be minimized by an appropriate system choice, a good training schedule, and the preparation of an appropriate IT infrastructure and database needed by the new system

Stealth databases : ensuring user-controlled queries in untrusted cloud environments

Sensitive data is increasingly being hosted online in ubiquitous cloud storage services. Recent advances in multi-cloud service integration through provider multiplexing and data dispersion have alleviated most of the associated risks for hosting files which are retrieved by users for further processing. However, for structured data managed in databases, many issues remain, including the need to perform operations directly on the remote data to avoid costly transfers. In this paper, we motivate the need for distributed stealth databases which combine properties from structure-preserving dispersed file storage for capacity-saving increased availability with emerging work on structure-preserving encryption for on-demand increased confidentiality with controllable performance degradation. We contribute an analysis of operators executing in map-reduce or map-carry-reduce phases and derive performance statistics. Our prototype, StealthDB, demonstrates that for typical amounts of personal structured data, stealth databases are a convincing concept for taming untrusted and unsafe cloud environments

An optimized computational model for multi-community-cloud social collaboration

PublishedCommunity Cloud Computing is an emerging and promising computing model for a specific community with common concerns, such as security, compliance and jurisdiction. It utilizes the spare resources of networked computers to provide the facilities so that the community gains services from the cloud. The effective collaboration among the community clouds offers a powerful computing capacity for complex tasks containing the subtasks that need data exchange. Selecting the best group of community clouds that are the most economy-efficient, communication-efficient, secured, and trusted to accomplish a complex task is very challenging. To address this problem, we first formulate a computational model for multi-community-cloud collaboration, namely MG3. The proposed model is then optimized from four aspects: minimizing the sum of access cost and monetary cost, maximizing the security-level agreement and trust among the community clouds. Furthermore, an efficient and comprehensive selection algorithm is devised to extract the best group of community clouds in MG3. Finally, the extensive simulation experiments and performance analysis of the proposed algorithm are conducted. The results demonstrate that the proposed algorithm outperforms the minimal set coverings based algorithm and the random algorithm. Moreover, the proposed comprehensive community clouds selection algorithm can guarantee good global performance in terms of access cost, monetary cost, security level and trust between user and community clouds

A gearbox model for processing large volumes of data by using pipeline systems encapsulated into virtual containers

Software pipelines enable organizations to chain applications for adding value to contents (e.g., confidentially, reliability, and integrity) before either sharing them with partners or sending them to the cloud. However, the pipeline components add overhead when processing large volumes of data, which can become critical in real-world scenarios. This paper presents a gearbox model for processing large volumes of data by using pipeline systems encapsulated into virtual containers. In this model, the gears represent applications, whereas gearboxes represent software pipelines. This model was implemented as a collaborative system that automatically performs Gear up (by using parallel patterns) and/or Gear down (by using in-memory storage) until all gears produce uniform data processing velocities. This model reduces delays and bottlenecks produced by the heterogeneous performance of applications included in software pipelines. The new container tool has been designed to encapsulate both the collaborative system and the software pipelines into a virtual container and deploy it on IT infrastructures. We conducted case studies to evaluate the performance of when processing medical images and PDF repositories. The incorporation of a capsule to a cloud storage service for pre-processing medical imagery was also studied. The experimental evaluation revealed the feasibility of applying the gearbox model to the deployment of software pipelines in real-world scenarios as it can significantly improve the end-user service experience when pre-processing large-scale data in comparison with state-of-the-art solutions such as Sacbe and Parsl.This work has been partially supported by the “Spanish Ministerio de Economia y Competitividad ” under the project grant TIN2016-79637-P “Towards Unification of HPC and Big Data paradigms”

SkyCDS: A resilient content delivery service based on diversified cloud storage

Cloud-based storage is a popular outsourcing solution for organizations to deliver contents to end-users. However, there is a need for contingency plans to ensure service provision when the provider either suffers outages or is going out of business. This paper presents SkyCDS: a resilient content delivery service based on a publish/subscribe overlay over diversified cloud storage. SkyCDS splits the content delivery into metadata and content storage flow layers. The metadata flow layer is based on publish-subscribe patterns for insourcing the metadata control back to content owner. The storage layer is based on dispersal information over multiple cloud locations with which organizations outsource content storage in a controlled manner. In SkyCDS, the content dispersion is performed on the publisher side and the content retrieving process on the end-user side (the subscriber), which reduces the load on the organization side only to metadata management. SkyCDS also lowers the overhead of the content dispersion and retrieving processes by taking advantage of multi-core technology. A new allocation strategy based on cloud storage diversification and failure masking mechanisms minimize side effects of temporary, permanent cloud-based service outages and vendor lock-in. We developed a SkyCDS prototype that was evaluated by using synthetic workloads and a study case with real traces. Publish/subscribe queuing patterns were evaluated by using a simulation tool based on characterized metrics taken from experimental evaluation. The evaluation revealed the feasibility of SkyCDS in terms of performance, reliability and storage space profitability. It also shows a novel way to compare the storage/delivery options through risk assessment. (C) 2015 Elsevier B.V. All rights reserved.The work presented in this paper has been partially supported by EU under the COST programme Action IC1305, Network for Sustainable Ultrascale Computing (NESUS)

Complying with Data Handling Requirements in Cloud Storage Systems

In past years, cloud storage systems saw an enormous rise in usage. However, despite their popularity and importance as underlying infrastructure for more complex cloud services, today's cloud storage systems do not account for compliance with regulatory, organizational, or contractual data handling requirements by design. Since legislation increasingly responds to rising data protection and privacy concerns, complying with data handling requirements becomes a crucial property for cloud storage systems. We present PRADA, a practical approach to account for compliance with data handling requirements in key-value based cloud storage systems. To achieve this goal, PRADA introduces a transparent data handling layer, which empowers clients to request specific data handling requirements and enables operators of cloud storage systems to comply with them. We implement PRADA on top of the distributed database Cassandra and show in our evaluation that complying with data handling requirements in cloud storage systems is practical in real-world cloud deployments as used for microblogging, data sharing in the Internet of Things, and distributed email storage.Comment: 14 pages, 11 figures; revised manuscript, accepted for publication in IEEE Transactions on Cloud Computin

Data storage security and privacy in cloud computing: A comprehensive survey

Cloud Computing is a form of distributed computing wherein resources and application platforms are distributed over the Internet through on demand and pay on utilization basis. Data Storage is main feature that cloud data centres are provided to the companies/organizations to preserve huge data. But still few organizations are not ready to use cloud technology due to lack of security. This paper describes the different techniques along with few security challenges, advantages and also disadvantages. It also provides the analysis of data security issues and privacy protection affairs related to cloud computing by preventing data access from unauthorized users, managing sensitive data, providing accuracy and consistency of data store

Mobile-cloud assisted video summarization framework for efficient management of remote sensing data generated by wireless capsule sensors

YesWireless capsule endoscopy (WCE) has great advantages over traditional endoscopy because it is portable and easy to use, especially in remote monitoring health-services. However, during the WCE process, the large amount of captured video data demands a significant deal of computation to analyze and retrieve informative video frames. In order to facilitate efficient WCE data collection and browsing task, we present a resource- and bandwidth-aware WCE video summarization framework that extracts the representative keyframes of the WCE video contents by removing redundant and non-informative frames. For redundancy elimination, we use Jeffrey-divergence between color histograms and inter-frame Boolean series-based correlation of color channels. To remove non-informative frames, multi-fractal texture features are extracted to assist the classification using an ensemble-based classifier. Owing to the limited WCE resources, it is impossible for the WCE system to perform computationally intensive video summarization tasks. To resolve computational challenges, mobile-cloud architecture is incorporated, which provides resizable computing capacities by adaptively offloading video summarization tasks between the client and the cloud server. The qualitative and quantitative results are encouraging and show that the proposed framework saves information transmission cost and bandwidth, as well as the valuable time of data analysts in browsing remote sensing data.Supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2012904)