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arXiv · 2111.09863

A Secure Experimentation Sandbox for the design and execution of trusted and secure analytics in the aviation domain

Dimitrios Miltiadou·Stamatis Pitsios·Dimitrios Spyropoulos·Dimitrios Alexandrou·Fenareti Lampathaki·Domenico Messina·Konstantinos Perakis
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Abstract

The aviation industry as well as the industries that benefit and are linked to it are ripe for innovation in the form of Big Data analytics. The number of available big data technologies is constantly growing, while at the same time the existing ones are rapidly evolving and empowered with new features. However, the Big Data era imposes the crucial challenge of how to effectively handle information security while managing massive and rapidly evolving data from heterogeneous data sources. While multiple technologies have emerged, there is a need to find a balance between multiple security requirements, privacy obligations, system performance and rapid dynamic analysis on large datasets. 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, integration and deep analysis of original and derivative data in a trusted and fair manner. Towards this end, a Secure Experimentation Sandbox has been designed and integrated in the 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 utilise the platform to conduct analytical experiments in closed-lab conditions.

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Dimitrios Miltiadou, Stamatis Pitsios, Dimitrios Spyropoulos, Dimitrios Alexandrou, Fenareti Lampathaki, Domenico Messina, Konstantinos Perakis. 2021-11-18. A Secure Experimentation Sandbox for the design and execution of trusted and secure analytics in the aviation domain. https://doi.org/10.1007/978-3-030-66922-5_8

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