Service Level Agreement-based GDPR Compliance and Security assurance in (multi)Cloud-based systems

Compliance with the new European General Data Protection Regulation (Regulation (EU) 2016/679) and security assurance are currently two major challenges of Cloud-based systems. GDPR compliance implies both privacy and security mechanisms definition, enforcement and control, including evidence collection. This paper presents a novel DevOps framework aimed at supporting Cloud consumers in designing, deploying and operating (multi)Cloud systems that include the necessary privacy and security controls for ensuring transparency to end-users, third parties in service provision (if any) and law enforcement authorities. The framework relies on the risk-driven specification at design time of privacy and security level objectives in the system Service Level Agreement (SLA) and in their continuous monitoring and enforcement at runtime.The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 644429 and No 780351, MUSA project and ENACT project, respectively. We would also like to acknowledge all the members of the MUSA Consortium and ENACT Consortium for their valuable help

Context-aware and automatic configuration of mobile devices in cloud-enabled ubiquitous computing

This is the author's accepted manuscript. The final publication is available at Springer via http://dx.doi.org/10.1007/s00779-013-0698-3. Copyright @ Springer-Verlag London 2013.Context-sensitive (or aware) applications have, in recent years, moved from the realm of possibilities to that of ubiquity. One exciting research area that is still very much in the realm of possibilities is that of cloud computing, and in this paper, we present our work, which explores the overlap of these two research areas. Accordingly, this paper explores the notion of cross-source integration of cloud-based, context-aware information in ubiquitous computing through a developed prototypical solution. Moreover, the described solution incorporates remote and automatic configuration of Android smartphones and advances the research area of context-aware information by harvesting information from several sources to build a rich foundation on which algorithms for context-aware computation can be based. Evaluation results show the viability of integrating and tailoring contextual information to provide users with timely, relevant and adapted application behaviour and content

A methodology for automatic derivation of cloud marketplace and cloud intelligence

University of Technology Sydney. Faculty of Engineering and Information Technology.From a consumer’s perspective, a cloud services marketplace is essential for cloud services discovery, selection, and composition. In practice, there are some private cloud services marketplaces, such as the Microsoft Azure marketplace, which are available for consumers belonging to a given vendor only. Nowadays, with the increase in the number of cloud services advertisements, and the adoption of cloud services, the cloud services consumer-base has grown and is projected to expand significantly over time. This increase defines the need for cloud services marketplace to enable effective interaction with cloud services users. A considerable amount of research has conducted in the area of cloud service selection and composition; however, the majority of this research is focused on developing algorithms (such as matching algorithms) and assumes the availability of cloud service information. Furthermore, little attention was given to the efficient discovery of cloud services over the World Wide Web (WWW). According to our literature, no research addresses the need for cloud services marketplace. Hence, this thesis proposes to provide an automatic derivation of cloud marketplace. The design of this marketplace includes a combination of the following modules: 1) cloud services harvesting module; 2) knowledge base for cloud service module; 3) cloud service trust derived intelligence module. The cloud services harvesting method is designed for harvesting cloud services advertisements from the web and building cloud services dataset. Such a dataset could be used by potential consumers for cloud services discovery and could be useful for future research in cloud selection, composition and recommender systems. Also, the developed cloud services repository could act as a knowledge source for constructing a standard ontology for cloud services. The knowledge base for cloud service module is designed for producing a solution toward cloud services marketplace to organise, publish and retrieve cloud services advertisements. This method involves semantically categories cloud services advertisements grounded on harvested web data to solve the issue of various cloud services advertisements. Also, this method includes the construction of the first commercial cloud services ontology-based repository for cloud services marketing. This repository contains service metadata that can be used to store service advertisements information which annotating to the domain-specific ontology concepts toward retrieving service advertisements more efficiently. The cloud services trust derived cloud Intelligence Module is designed to automatically analyzing the sentiment of cloud reviews to provide the potential consumers with real quality of service (Quality of Experience) information when making the buying decision. Also, building cloud reviews classifier to automatically classify the reviews: positive, neutral or negative using supervised machine learning algorithms. The result of this thesis will be an intelligent methodology for an automated derivation of the cloud marketplace: cloud services harvester, cloud services knowledge base, and Quality of Experience of cloud services. This methodology will be useful to the potential consumers, cloud providers, and the research community, as it will provide easy access to cloud services advertisements information

Monitoring spatial sustainable development: Semi-automated analysis of satellite and aerial images for energy transition and sustainability indicators

Solar panels are installed by a large and growing number of households due to the convenience of having cheap and renewable energy to power house appliances. In contrast to other energy sources solar installations are distributed very decentralized and spread over hundred-thousands of locations. On a global level more than 25% of solar photovoltaic (PV) installations were decentralized. The effect of the quick energy transition from a carbon based economy to a green economy is though still very difficult to quantify. As a matter of fact the quick adoption of solar panels by households is difficult to track, with local registries that miss a large number of the newly built solar panels. This makes the task of assessing the impact of renewable energies an impossible task. Although models of the output of a region exist, they are often black box estimations. This project's aim is twofold: First automate the process to extract the location of solar panels from aerial or satellite images and second, produce a map of solar panels along with statistics on the number of solar panels. Further, this project takes place in a wider framework which investigates how official statistics can benefit from new digital data sources. At project completion, a method for detecting solar panels from aerial images via machine learning will be developed and the methodology initially developed for BE, DE and NL will be standardized for application to other EU countries. In practice, machine learning techniques are used to identify solar panels in satellite and aerial images for the province of Limburg (NL), Flanders (BE) and North Rhine-Westphalia (DE).Comment: This document provides the reader with an overview of the various datasets which will be used throughout the project. The collection of satellite and aerial images as well as auxiliary information such as the location of buildings and roofs which is required to train, test and validate the machine learning algorithm that is being develope

Integrated context-aware and cloud-based adaptive home screens for android phones

This is the post-print version of this Article. The official published version can be accessed from the link below - Copyright @ 2011 Springer VerlagThe home screen in Android phones is a highly customizable user interface where the users can add and remove widgets and icons for launching applications. This customization is currently done on the mobile device itself and will only create static content. Our work takes the concept of Android home screen [3] one step further and adds flexibility to the user interface by making it context-aware and integrated with the cloud. Overall results indicated that the users have a strong positive bias towards the application and that the adaptation helped them to tailor the device to their needs by using the different context aware mechanisms