MISP: Model for IaaS Security and Privacy

Paradigm shift towards cloud computing offers plethora of advantages both for cloud users and Cloud Service Provider (CSP). For cloud users, it offers saving of cost, scaling of resources, pay per use, elastic and on-demand services. On the other hand, it offers centralized resource management and provisioning of operations, safety and security for CSP. By holding multiple virtual IT resources (CPUs, storage servers, network components and software) over the internet, Infrastructure-as-a-Service (IaaS) serves as fundamental layer for all other delivery models. Along with benefits of IaaS, there exists several security and privacy issues and threats to confidentiality, integrity, authentication, access control and availability. In this paper, detailed study of IaaS components, associated security and privacy issues are explored and counter measures for the same are determined. Furthermore, as a result of the study, Model for IaaS Security and Privacy (MISP) is proposed. The model presents a cubical structure and adds more features than the existing models to enhance the security and privacy of data and operations and guide security assessment for safer adoption by enterprises.

MISP: Model for IaaS Security and Privacy

Paradigm shift towards cloud computing offers plethora of advantages both for cloud users and Cloud Service Provider (CSP). For cloud users, it offers saving of cost, scaling of resources, pay per use, elastic and on-demand services. On the other hand, it offers centralized resource management and provisioning of operations, safety and security for CSP. By holding multiple virtual IT resources (CPUs, storage servers, network components and software) over the internet, Infrastructure-as-a-Service (IaaS) serves as fundamental layer for all other delivery models. Along with benefits of IaaS, there exists several security and privacy issues and threats to confidentiality, integrity, authentication, access control and availability. In this paper, detailed study of IaaS components, associated security and privacy issues are explored and counter measures for the same are determined. Furthermore, as a result of the study, Model for IaaS Security and Privacy (MISP) is proposed. The model presents a cubical structure and adds more features than the existing models to enhance the security and privacy of data and operations and guide security assessment for safer adoption by enterprises

EBRAINS Live Papers - Interactive resource sheets for computational studies in neuroscience

We present here an online platform for sharing resources underlying publications in neuroscience. It enables authors to easily upload and distribute digital resources, such as data, code, and notebooks, in a structured and systematic way. Interactivity is a prominent feature of the Live Papers, with features to download, visualise or simulate data, models and results presented in the corresponding publications. The resources are hosted on reliable data storage servers to ensure long term availability and easy accessibility. All data are managed via the EBRAINS Knowledge Graph, thereby helping maintain data provenance, and enabling tight integration with tools and services offered under the EBRAINS ecosystem.

Survey Report on Cloud Security

Cloud computing is an architecture. Cloud DBMS is a distributed database that provides computing service to a group of shared resources namely networks, storage, servers, services and applications through the internet on requirement and pay as you use access without physically acquiring them. For resources, software and information over a network the web infrastructure is shared. The database stored in the cloud can be accessed and computed from anywhere and it is used as a storage location. In this paper I have discussed some of the literature review of cloud, the cloud topics and the papers published by the authors in the appropriate years, the existing methodology used with its drawbacks and proposed methodology with its benefits in which we can improve it in future.

An Effective Techniques Using Apriori and Logistic Methods in Cloud Computing

This paper presents a creativity data prefetching scheme on the loading servers in distributed file systems for cloud computing. The server will get and piggybacked the frequent data from the client system, after analyzing the fetched data is forward to the client machine from the server. To place this technique to work, the data about client nodes is piggybacked onto the real client I/O requests, and then forwarded to the relevant storage server. Next, dual prediction algorithms have been proposed to calculation future block access operations for directing what data should be fetched on storage servers in advance. Finally, the prefetching data can be pressed to the relevant client device from the storage server. Over a series of evaluation experiments with a group of application benchmarks, we have demonstrated that our presented initiative prefetching technique can benefit distributed file systems for cloud environments to achieve better I/O performance. In particular, configuration-limited client machines in the cloud are not answerable for predicting I/O access operations, which can certainly contribute to preferable system performance on them.

An Enhanced Data Storage Technique on Cloud Computing

Exercising a collection of similar numerous easy to get sources and resources over the internet is termed as Cloud Computing A Cloud storage system is basically a storage system over a large scale that consist of many independent storage servers. During recent years a huge changes and adoption is seen in cloud computing so security has become one of the major concerns in it. As Cloud computing works on third party system so security concern is there not only for customers but also for service providers. In this paper we have discussed about Cryptography i.e., encrypting messages into certain forms, it’s algorithms including symmetric and asymmetric algorithm and hashing, its architecture, and advantages of cryptography.

An Enhanced Data Storage Technique on Cloud Computing

Exercising a collection of similar numerous easy to get sources and resources over the internet is termed as Cloud Computing A Cloud storage system is basically a storage system over a large scale that consist of many independent storage servers. During recent years a huge changes and adoption is seen in cloud computing so security has become one of the major concerns in it. As Cloud computing works on third party system so security concern is there not only for customers but also for service providers. In this paper we have discussed about Cryptography i.e., encrypting messages into certain forms, it’s algorithms including symmetric and asymmetric algorithm and hashing, its architecture, and advantages of cryptography.

EFURMS: An Efficient Scheme for File Upload and Ranked Multi-Keyword Search over Encrypted Data in Cloud

Cloud based services provide scalable storage capacities and enormous computing capability to enterprises and individuals to support big data operations in different sectors like banking, scientific research and health care. Therefore many data owners are interested to outsource their data to cloud storage servers due to their huge advantage in data processing. However, as the banking and health records usually contain sensitive data, there are privacy concerns if the data gets leaked to un-trusted third parties in cloud storage. To protect data from leakage, the widely used technique is to encrypt the data before uploading into cloud storage servers. The traditional methods implemented by many authors consumes more time to outsource the data and searching for a document is also time consuming. Sometimes there may be chances of data leakage due to insufficient security. To resolve these issues, in the current VPSearch(VPS) scheme is implemented, which provides features like verifiability of search results and privacy preservation. With its features the current system consumes more time for file uploading and index generation, which slows down the searching process. In the existing VPS scheme time minimization to efficiently search for a particular document is a challenging task on the cloud. To resolve all the above drawbacks, we have designed an index generation scheme using a tree structure along with a search algorithm using Greedy Depth-first technique, that reduces the time for uploading files and file searching time. The newly implemented scheme minimizes the time required to form the index tree file for set of files in the document which are to be uploaded and helps in storing the files in a index tree format. These techniques result in reducing the document upload time and speeding up the process of accessing data efficiently using multi-keyword search with top-'K' value.

Mapping Datasets to Object Storage System

Access libraries such as ROOT[1] and HDF5[2] allow users to interact with datasets using high level abstractions, like coordinate systems and associated slicing operations. Unfortunately, the implementations of access libraries are based on outdated assumptions about storage systems interfaces and are generally unable to fully benefit from modern fast storage devices. For example, access libraries often implement buffering and data layout that assume that large, single-threaded sequential access patterns are causing less overall latency than small parallel random access: while this is true for spinning media, it is not true for flash media. The situation is getting worse with rapidly evolving storage devices such as non-volatile memory and ever larger datasets. This project explores distributed dataset mapping infrastructures that can integrate and scale out existing access libraries using Ceph’s extensible object model, avoiding re-implementation or even modifications of these access libraries as much as possible. These programmable storage extensions coupled with our distributed dataset mapping techniques enable: 1) access library operations to be offloaded to storage system servers, 2) the independent evolution of access libraries and storage systems and 3) fully leveraging of the existing load balancing, elasticity, and failure management of distributed storage systems like Ceph. They also create more opportunities to conduct storage server-local optimizations specific to storage servers. For example, storage servers might include local key/value stores combined with chunk stores that require different optimizations than a local file system. As storage servers evolve to support new storage devices like non-volatile memory, these server-local optimizations can be implemented while minimizing disruptions to applications. We will report progress on the means by which distributed dataset mapping can be abstracted over particular access libraries, including access libraries for ROOT data, and how we address some of the challenges revolving around data partitioning and composability of access operations.