http://www.parkjonghyuk.net/lecture/2015-1st-lecture/networksecurity/Paper2.pdf

Security in cloud computing

Big Data has emerged in the past few years as a new paradigm providing abundant data and opportunities to improve and/or enable research and decision-support applications with unprecedented value for digital earth applications including business, sciences and engineering. At the same time, Big Data presents challenges for digital earth to store, transport, process, mine and serve the data. Cloud computing provides fundamental support to address the challenges with shared computing resources including computing, storage, networking and analytical software; the application of these resources has fostered impressive Big Data advancements. This paper surveys the two frontiers – Big Data and cloud computing – and reviews the advantages and consequences of utilizing cloud computing to tackling Big Data in the digital earth and relevant science domains. From the aspects of a general introduction, sources, challenges, technology status and research opportunities, the following observations are offered: (i...

https://tandfonline.com/doi/pdf/10.1080/17538947.2016.1239771

Big Data and cloud computing: innovation opportunities and challenges

In the last few years, the appealing features of cloud computing have been fueling the integration of cloud environments in the industry, which has been consequently motivating the research on related technologies by both the industry and the academia. The possibility of paying-as-you-go mixed with an on-demand elastic operation is changing the enterprise computing model, shifting on-premises infrastructures to off-premises data centers, accessed over the Internet and managed by cloud hosting providers. Regardless of its advantages, the transition to this computing paradigm raises security concerns, which are the subject of several studies. Besides of the issues derived from Web technologies and the Internet, clouds introduce new issues that should be cleared out first in order to further allow the number of cloud deployments to increase. This paper surveys the works on cloud security issues, making a comprehensive review of the literature on the subject. It addresses several key topics, namely vulnerabilities, threats, and attacks, proposing a taxonomy for their classification. It also contains a thorough review of the main concepts concerning the security state of cloud environments and discusses several open research topics.

/pdf/security-issues-in-cloud-environments-a-survey-1t9z0if3y4.pdf

Security issues in cloud environments: a survey

A survey on cloud computing security

In federated learning (FL), devices contribute to the global training by uploading their local model updates via wireless channels. Due to limited computation and communication resources, device scheduling is crucial to the convergence rate of FL. In this paper, we propose a joint device scheduling and resource allocation policy to maximize the model accuracy within a given total training time budget for latency constrained wireless FL. A lower bound on the reciprocal of the training performance loss, in terms of the number of training rounds and the number of scheduled devices per round, is derived. Based on the bound, the accuracy maximization problem is solved by decoupling it into two sub-problems. First, given the scheduled devices, the optimal bandwidth allocation suggests allocating more bandwidth to the devices with worse channel conditions or weaker computation capabilities. Then, a greedy device scheduling algorithm is introduced, which selects the device consuming the least updating time obtained by the optimal bandwidth allocation in each step, until the lower bound begins to increase, meaning that scheduling more devices will degrade the model accuracy. Experiments show that the proposed policy outperforms state-of-the-art scheduling policies under extensive settings of data distributions and cell radius.

Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning

It is well-known that cloud computing has many potential advantages and many enterprise applications and data are migrating to public or hybrid cloud. But regarding some business-critical applications, the organizations, especially large enterprises, still wouldn't move them to cloud. The market size the cloud computing shared is still far behind the one expected. From the consumers' perspective, cloud computing security concerns, especially data security and privacy protection issues, remain the primary inhibitor for adoption of cloud computing services. This paper provides a concise but all-round analysis on data security and privacy protection issues associated with cloud computing across all stages of data life cycle. Then this paper discusses some current solutions. Finally, this paper describes future research work about data security and privacy protection issues in cloud.

Data Security and Privacy Protection Issues in Cloud Computing

Hierarchical classification based on coarse- to fine-grained knowledge transfer

Reasoning over knowledge graph explores valuable information for amounts of tasks. However, most methods adopt the coarse-grained and single representation of each entity for reasoning, ignoring simultaneously processing various semantics contained in internal information and external information. On the one hand, the surrounding nodes and relations existing in the graph structure express the internal information of the entity, which contains abundant graph context information, but the extracted internal features are still limited. On the other hand, different scenarios as the external information focus on different aspects of the certain entity, meanwhile the external information should have message interaction with the internal information to learn the adaptive embedding, both of which are seldom considered by the existing methods. In this paper, we propose a Graph Intention Neural Network (GINN) for knowledge graph reasoning to explore fine-grained entity representations, which use external-intention and internal-intention simultaneously. For external-intention, a novel constructed matrix is used to calculate the triple-attention that determines the aggregated information to learn different embeddings adapting to the different scenarios. Furthermore, a communication bridge is leveraged to have message interaction between the external information and the internal information. For the internal-intention, the surrounding nodes and relations are integrated to update the entity embedding with the consideration of the interaction features between the external and internal information. The triple-attention can capture relevancy among the reasoning hops, which contributes to figuring out reasonable paths. We evaluate our approach on real-world datasets, achieving better performance compared to the state-of-the-art methods and showing plausible interpretability for the results.

Hong Zhao

Papers

Data Security and Privacy Protection Issues in Cloud Computing

Hierarchical classification based on coarse- to fine-grained knowledge transfer

Graph Intention Neural Network for Knowledge Graph Reasoning

Knowledge transfer based hierarchical few-shot learning via tree-structured knowledge graph

Simulate Human Thinking: Cognitive Knowledge Graph Reasoning for Complex Question Answering