A survey of security issues for cloud computing

We review the state of the art of privacy-preserving schemes for ad hoc social networks including mobile social networks (MSNs) and vehicular social networks (VSNs). Specifically, we select and examine in-detail 33 privacy-preserving schemes developed for or applied in the context of ad hoc social networks. Based on novel schemes published between 2008 and 2016, we survey privacy preservation models including location privacy, identity privacy, anonymity, traceability, interest privacy, backward privacy, and content oriented privacy. Recent significant attacks of leaking privacy, countermeasures, and game theoretic approaches in VSNs and MSNs are summarized in the form of tables. In addition, an overview of recommendations for further research is provided. With this survey, readers can acquire a thorough understanding of research trends in privacy-preserving schemes for ad hoc social networks.

Privacy-Preserving Schemes for Ad Hoc Social Networks: A Survey

In this paper, we review the state of the art of privacy-preserving schemes for ad hoc social networks, including, mobile social networks (MSNs) and vehicular social networks (VSNs). Specifically, we select and in-detail examine thirty-three privacy preserving schemes developed for or applied in the context of ad hoc social networks. These schemes are published between 2008 and 2016. Based on this existing privacy preservation schemes, we survey privacy preservation models, including location privacy, identity privacy, anonymity, traceability, interest privacy, backward privacy, and content oriented privacy. The recent important attacks of leaking privacy, countermeasures, and game theoretic approaches in VSNs and MSNs are summarized in form of tables. In addition, an overview of recommendations for further research is also provided. With this survey, readers can have a more thorough understanding of research trends in privacy-preserving schemes for ad hoc social networks

Privacy-preserving schemes for Ad Hoc Social Networks: A survey

Reference architectures (RAs) are useful tools to understand and build complex systems, and many cloud providers and software product vendors have developed versions of them. RAs describe at an abstract level (no implementation details) the main features of their cloud systems. Security is a fundamental concern in clouds and several cloud vendors provide security reference architectures (SRAs) to describe the security features of their services. A SRA is an abstract architecture describing a conceptual model of security for a cloud system and provides a way to specify security requirements for a wide range of concrete architectures. We propose here a method to build a SRA for clouds defined using UML models and patterns, which goes beyond existing models in providing a global view and a more precise description. We present a metamodel as well as security and misuse patterns for this purpose. We validate our approach by showing that it can describe more precisely existing models and that it has a variety of uses. We describe in detail one of these uses, a way of evaluating the security level of a SRA.

https://link.springer.com/content/pdf/10.1007%2Fs00766-014-0218-7.pdf

Building a security reference architecture for cloud systems

A Neural Network Ensemble (NNE) combines the outputs of several individually trained neural networks in order to improve generalization performance. This article summarizes different approaches on the development and the latest studies on NNE. The introduction of the basic principles of NNE is followed by detailed descriptions of individual neural network generation method, conclusion generation method and fusion based on granular computing and NNE. In addition, for each of these methods we provide a short taxonomy in terms of their relevant characteristics, and analyze several of NNE applications, classic algorithms and contributions on various fields.

Research and development of neural network ensembles: a survey

Security in microservice-based systems: A Multivocal literature review

Two security patterns for clouds are presented: Secure Virtual Machine Image Repository, that controls the introduction or creation of malicious virtual machine images, and Cloud Policy Management Point, that defines a dashboard for the security administrator to control access to cloud resources.

/pdf/two-patterns-for-cloud-computing-secure-virtual-machine-3minmzq1on.pdf

Two patterns for cloud computing: secure virtual machine image repository and cloud policy management point

Security is a fundamental concern in clouds and several cloud vendors provide Security Reference Architectures (SRAs) to describe the security level of their services. A SRA is an abstract architecture without implementation details showing a conceptual model of security for a cloud system. In general, Reference Architectures (RAs) are becoming useful tools to understand and build complex systems. We propose here a Security Reference Architecture (SRA), defined using UML models and patterns, incorporating a specific approach to build secure systems. We present a metamodel and possible patterns to conceptualize the approach. We also describe some uses for this SRA, including its value for Service Level Agreements (SLAs), service certification, monitoring, and security evaluation. We show this latter use in some detail.

A security reference architecture for cloud systems

Ensemble learning has gained considerable attention in different tasks including regression, classification and clustering. Adaboost and Bagging are two popular approaches used to train these models. The former provides accurate estimations in regression settings but is computationally expensive because of its inherently sequential structure, while the latter is less accurate but highly efficient. One of the drawbacks of the ensemble algorithms is the high computational cost of the training stage. To address this issue, we propose a parallel implementation of the Resampling Local Negative Correlation (RLNC) algorithm for training a neural network ensemble in order to acquire a competitive accuracy like that of Adaboost and an efficiency comparable to that of Bagging. We test our approach on both synthetic and real datasets from the UCI and Statlib repositories for the regression task. In particular, our fine-grained parallel approach allows us to achieve a satisfactory balance between accuracy and parallel efficiency.

Raúl Monge

Papers

Building a security reference architecture for cloud systems

Security in microservice-based systems: A Multivocal literature review

Two patterns for cloud computing: secure virtual machine image repository and cloud policy management point

A security reference architecture for cloud systems

Parallel Approach for Ensemble Learning with Locally Coupled Neural Networks