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Machine Learning and the Legal Framework for the Use of Passenger Name Record Data

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Intelligent Technologies and Applications (INTAP 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1382))

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Abstract

The processing of passenger name records (PNR) for security purposes on a European level was officially announced in 2016. Since then, ongoing legal discussions about PNR data focus mainly on its collection and the impact on data protection. The focus of this paper lies on a less-discussed aspect of the legal framework: the processing of PNR data and the different technological approaches it allows for. The following analysis of the German implementation of the European Directive on PNR data processing shows that it is open to the use of two different technological approaches: theory-based and machine learning methods. Taking this into account can provide a perspective that is mostly lacking in current legal debates about PNR data but can be an important addition since different technological approaches might shift the focus from data protection concerns to some aspects of technologies like machine learning. The paper also addresses a need for more technical research on the topic.

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Notes

  1. 1.

    107th Congress (2001-2002), p. 1447 – Aviation and Transportation Security Act.

  2. 2.

    BGBl. Part 1: Nr. 34 (2017), 1484.

  3. 3.

    See p. 28 of BT-Drs. 18/11501.

  4. 4.

    AG Mengozzi, in: ECJ, Opinion 1/15, EU:C:2016:656, point 252.

  5. 5.

    BT-Drs. 18/11501, p. 28.

  6. 6.

    In computer sciences, the terms “features” or “attributes” are often used to describe the properties of a model. In mathematics, the term “variable” can sometimes be used for this purpose. In the following, the term “criteria” is used since it is also used in Section 4 (3) second sentence FlugDaG: “The patterns shall contain […] criteria”.

  7. 7.

    BT-Drs. 19/12858, p. 4.

  8. 8.

    BT-Drs. 18/11501, p. 23.

  9. 9.

    BT-Drs. 19/12975, p. 7.

  10. 10.

    Causality in this context is not understood as a definite causal link between cause and event, such as the term may be known in the natural sciences. Instead, a limited concept of causality is applied, which stands for an observed regularity between certain characteristics and certain offenses in the sense of plausible cause-and-effect relationships. Such a concept of causality stands for the assumption that certain causes make the occurrence of an event more probable and is understood, therefore, as probabilistic causality, such as described by [19], p. 87-97. When analyzing behavioral causes, it can be difficult to work with stricter concepts of causality, see [20] p. 1098, with further evidence.

  11. 11.

    BT-Drs. 18/11501, p. 29.

  12. 12.

    For a similar description of patterns in a different context of police work, see [17] p. 250.

  13. 13.

    https://tinyurl.com/yyd27n2x, last accessed on 30.1.2021.

  14. 14.

    EU Council 6300/19, 15.2.2019, p. 8 ff., see also [5], p. 12.

  15. 15.

    UN Homepage, https://www.un.org/cttravel/goTravel, last accessed on 30.1.2021.

  16. 16.

    EU Council 10139/18, 21.6.2018, p. 3.

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Authors and Affiliations

  1. Faculty of Law, Hamburg University, 20148, Hamburg, Germany

    Iva Kostov

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  1. Iva Kostov
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Correspondence to Iva Kostov .

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  1. NTNU, Gjøvik, Norway

    Sule Yildirim Yayilgan

  2. The Islamia University of Bahawalpur, Punjab, Pakistan

    Imran Sarwar Bajwa

  3. University of Agder, Kristiansand, Norway

    Filippo Sanfilippo

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Kostov, I. (2021). Machine Learning and the Legal Framework for the Use of Passenger Name Record Data. In: Yildirim Yayilgan, S., Bajwa, I.S., Sanfilippo, F. (eds) Intelligent Technologies and Applications. INTAP 2020. Communications in Computer and Information Science, vol 1382. Springer, Cham. https://doi.org/10.1007/978-3-030-71711-7_33

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