Today’s aviation operations utilize a set of large, heterogeneous, widely-distributed systems which are sometimes even composed of isolated sub-systems. These are highly complex and very difficult to model analytically, especially considering the interactions between them. Often, detailed data about these systems is needed to understand and benchmark their performance, set up targets, make policies or even plan shared network resources. In the last decade, access has improved to this type of data as well as the computing infrastructures required to store and perform complex calculations with such data. Some of those calculations are, for instance, machine learning algorithms which have also proven their usefulness. Aviation researchers are implementing solutions based on the latest deep learning techniques (DataScience.aero, 2018).
That said, large datasets are not as available to aviation data researchers compared to data availability in other fields. Data science researchers face challenges related to the diversity of inhomogeneous data sources and the large volume of information to be handled and represented. However, the confidentiality of the datasets has historically been the most difficult barrier to data accessibility as most data owners have refused to provide access to significantly large datasets.
A potential approach to overcome these barriers is to apply state-of-the-art cryptography techniques in aviation data solutions. By painting some air traffic management data science problems as cryptography systems, and utilizing novel crypto-based solutions, the confidentiality barrier can be overcome without breaking confidentiality requirements. Private data could be used in ATM procedures and systems. We believe the ATM community could benefit from working together with cryptography experts in finding adequate solutions for the aviation sector that respect the current safety levels. To foster this line of activity, we think it´s important to promote the PhD opportunity opened by TADOREA in collaboration with the Technical University of Madrid – Telematics Department.