Skip to main content

A Secure Experimentation Sandbox for the Design and Execution of Trusted and Secure Analytics in the Aviation Domain

  • Conference paper
  • First Online:
Security and Privacy in New Computing Environments (SPNCE 2020)


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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others


  1. Airbus: Data revolution in aviation. Accessed 23 July 2020

  2. Wyman, O.: Aviation’s data science revolution (2017). Accessed 23 July 2020

  3. Ajah, I.A., Nweke, H.F.: Big data and business analytics: trends, platforms, success factors and applications. Big Data Cogn. Comput. 3(2), 32 (2019)

    Article  Google Scholar 

  4. da Silva, T.L.C., et al.: Big data analytics technologies and platforms: a brief review. In: Latin America Data Science Workshop, 44th International Conference on Very Large Data Bases, Brazil (2018)

    Google Scholar 

  5. Amalina, F., et al.: Blending big data analytics: review on challenges and a recent study. IEEE Access 8, 3629–3645 (2019)

    Article  Google Scholar 

  6. Benjelloun, F.Z., Lahcen, A.A.: Big data security: challenges, recommendations and solutions. In: Web Services: Concepts, Methodologies, Tools, and Applications, pp. 25–38. IGI Global (2019)

    Google Scholar 

  7. Oussous, A., Benjelloun, F.Z., Lahcen, A.A.: Belfkih, S: Big data technologies: a survey. J. King Saud Univ.-Comput. Inf. Sci. 30(4), 431–448 (2018)

    Google Scholar 

  8. NIST Big Data Public Working Group: NIST Big Data Interoperability Framework: Volume 4, Security and Privacy Version 2 (No. NIST Special Publication (SP) 1500-4r1), National Institute of Standards and Technology (2018)

    Google Scholar 

  9. Venkatraman, S., Venkatraman, R.: Big data security challenges and strategies. AIMS Math. 4(3), 860–879 (2019)

    Article  Google Scholar 

  10. Nelson, B., Olovsson, T.: Security and privacy for big data: a systematic literature review. In: 2016 IEEE International Conference on Big Data (Big Data), pp. 3693–3702 (2016)

    Google Scholar 

  11. Chidambararajan, B., Kumar, M.S., Susee, M.S.: Big data privacy and security challenges in industries. Int. Res. J. Eng. Technol. 6(4), 1991 (2019)

    Google Scholar 

  12. ICARUS EC H2020 project Homepage. Accessed 20 June 2020

  13. ICARUS: Demonstrators Execution Scenarios and Readiness Documentation. EC H2020 ICARUS project (2019)

    Google Scholar 

Download references


ICARUS project is being funded by the European Commission under the Horizon 2020 Programme (Grant Agreement No 780792).

Author information

Authors and Affiliations


Corresponding author

Correspondence to Dimitrios Miltiadou .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Miltiadou, D. et al. (2021). A Secure Experimentation Sandbox for the Design and Execution of Trusted and Secure Analytics in the Aviation Domain. In: Wang, D., Meng, W., Han, J. (eds) Security and Privacy in New Computing Environments. SPNCE 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 344. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-66921-8

  • Online ISBN: 978-3-030-66922-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics