Research Article
A Secure Experimentation Sandbox for the Design and Execution of Trusted and Secure Analytics in the Aviation Domain
@INPROCEEDINGS{10.1007/978-3-030-66922-5_8, author={Dimitrios Miltiadou and Stamatis Pitsios and Dimitrios Spyropoulos and Dimitrios Alexandrou and Fenareti Lampathaki and Domenico Messina and Konstantinos Perakis}, title={A Secure Experimentation Sandbox for the Design and Execution of Trusted and Secure Analytics in the Aviation Domain}, proceedings={Security and Privacy in New Computing Environments. Third EAI International Conference, SPNCE 2020, Lyngby, Denmark, August 6-7, 2020, Proceedings}, proceedings_a={SPNCE}, year={2021}, month={1}, keywords={Big data Security Privacy Cybersecurity Data analytics}, doi={10.1007/978-3-030-66922-5_8} }- Dimitrios Miltiadou
Stamatis Pitsios
Dimitrios Spyropoulos
Dimitrios Alexandrou
Fenareti Lampathaki
Domenico Messina
Konstantinos Perakis
Year: 2021
A Secure Experimentation Sandbox for the Design and Execution of Trusted and Secure Analytics in the Aviation Domain
SPNCE
Springer
DOI: 10.1007/978-3-030-66922-5_8
Abstract
The undergoing digital transformation of the aviation industry is driven by the rise of cyber-physical systems and sensors and their massive deployment in airplanes, the proliferation of autonomous drones and next-level interfaces in the airports, connected aircrafts-airports-aviation ecosystems and is acknowledged as one of the most significant step-function changes in the aviation history. The aviation industry as well as the industries that benefit and are highly dependent or linked to it (e.g. tourism, health, security, transport, public administration) are ripe for innovation in the form of Big Data analytics. Leveraging Big Data requires the effective and efficient analysis of huge amounts of unstructured data that are harnessed and processed towards revealing trends, unseen patterns, hidden correlations, and new information, and towards immediately extracting knowledgeable information that can enable prediction and decision making. Conceptually, the big data lifecycle can be divided into three main phases: i) the data acquisition, ii) the data storage and iii) the data analytics. For each phase, the number of available big data technologies and tools that exploit these technologies is constantly growing, while at the same time the existing tools are rapidly evolving and empowered with new features. However, the Big Data era comes with new challenges and one of the crucial challenges faced nowadays is how to effectively handle information security while managing massive and rapidly evolving data from heterogeneous data sources. While multiple technologies and techniques have emerged, there is a need to find a balance between multiple security requirements, privacy obligations, system performance and rapid dynamic analysis on diverse large data sets. The current paper aims to introduce the ICARUS Secure Experimentation Sandbox of the ICARUS platform. The ICARUS platform aims to provide a big data-enabled platform that aspires to become an “one-stop shop” for aviation data and intelligence marketplace that provides a trusted and secure “sandboxed” analytics workspace, allowing the exploration, curation, integration and deep analysis of original, synthesized and derivative data characterized by different velocity, variety and volume in a trusted and fair manner. Towards this end, a Secure Experimentation Sandbox has been designed and integrated in the holistic ICARUS platform offering, that enables the provisioning of a sophisticated environment that can completely guarantee the safety and confidentiality of data, allowing to any interested party to utilize the platform to conduct analytical experiments in closed-lab conditions.
