SecureCloud addresses the confidentiality, integrity and availability of applications executed in the cloud. Data at rest or in transit on the network is already nowadays protected by encryption. The main problem that we face is how to ensure the confidentiality of data while being processed. Our approach is based on upcoming hardware extensions of commodity CPUs like Intel's Secure Guard Extensions (SGX). By the help of these hardware extensions, we reduce the trusted computing base dramatically by excluding from it the millions of lines of source code of the cloud stack, operating systems and hypervisor. This permits us to ensure the confidentiality of computations even if the computers are under a different administrative control (like a cloud provider) or there is no physical security of the computers. Moreover, we ensure the confidentiality even if attackers would take control of the cloud stack, the hypervisor or the operating systems. As long as the hardware extensions of the CPU can be trusted, we can ensure the confidentiality of the computations. SecureCloud focuses on ensuring the confidential and dependable processing of Big Data. To keep the trusted computing base small, we use the concept of microservices: only the application logic that processes data (e.g., operators) is protected while all functionality that, e.g., shuffles and stores encrypted data is outside the trusted computing base. By monitoring the microservices, we can restart services that run on compromised hosts. We will evaluate and demonstrate our approach in the context of smart grids. In this use case context, we need to run across a physically distributed computing infrastructure with no or little physical security and partly untrusted administrators. We need to process large volumes of data and this big data processing would benefit by partial offloading into the cloud. In SecureCloud, we will show how to do this in a secure fashion even if clouds are untrusted.

There is an increasing need to develop data intensive applications able to manage more and more amounts of data coming from distributed and heterogeneous sources effectively, quickly, correctly, and securely. However, the current adoption of Cloud Computing paradigm is not fully appropriate to store and analyse such data: latency, security, and compliance are still significant barriers. At the same time, Fog Computing has emerged as a paradigm promising to fully exploit the potential of the edge of the network involving traditional devices as well as new generation of smart devices, which can process data closer to where they are produced and/or consumed but which cannot ensure the same reliability and scalability as cloud computing offers. The goal of DITAS is to propose a framework, composed by an SDK and an execution environment, which aims to overcome the barriers that now hamper the adoption of Cloud Computing and increase the adoption of Fog computing by exploiting the full potential of these two paradigms in a synergic way. This will support the development and execution of data-intensive application that are now – and even more in the future – crucial for organizations and companies that want to manage their data in an efficient, reliable, scalable, and secure manner. Abstractions provided in DITAS with Data Virtualization and Data Utility will expose the data to be managed by the application in terms of Virtual Data Containers which hide the complexity of the underlying infrastructure composed of heterogeneous data sources, smart devices, traditional servers, and sensor networks Distribution could also change dynamically. Conversely, Virtual Data Containers offer to developers the possibility to express requirements on data in terms of performance, quality, security and privacy thus to focus only on the application logic, leaving to the DITAS execution environment the responsibility of finding, processing, and delivering the data according to user needs

Technological advances in remote sensing have increased the availability of satellite images with different spatiotemporal and spectral characteristics.There is difficulty for retrieving the most appropriate data for each user's needs.One key challenge is to connect the quantitative information of the EO images with the qualitative (high-level user queries) and be able to mine these connections in big archives.An inherent question arises; how to retrieve EO images based on user semantically aware questions.Content based EO image retrieval techniques have been introduced for bridging the gap between low-level image features and high-level queries.The main constraint of the existing approaches is the generalization of the problem.The formulated ontologies are not focused on the constraints of EO images.The main objective of SEO-DWARF is to realize the content-based search of EO images on an application specific basis.The marine application domain and data from Sentinels 1,2,3, ENVISAT will be used.Queries such as “Calculate the rate of increasing chlorophyll in the NATURA area” will be answered by the SEO-DWARF, helping users to retrieve the appropriate EO images for their specific needs or alert them when a specific phenomenon occurs.The research contains the:a) ontology formalization for the specific research topics,b) determination of the semantic queries for the application domains,c) algorithm development for extracting metadata from the EO images,d) design of an architecture of the platform to perform the semantic image retrieval and storage and management of the extracted metadata.All four aspects will be integrated in an innovative and user-friendly web based platform enabling the users to retrieve images for marine applications or register for a semantic alert.A strong and experienced research team, of 4 academic and 5 industrial partners, coming from Greece(3), Italy(2), Germany(1), France(1), Cyprus(1) and Switzerland(1) constitute the project’s consortium.