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An Example of Privacy and Data Protection Best Practices for Biometrics Data Processing in Border Control: Lesson Learned from SMILE

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Abstract

Biometric recognition is a highly adopted technology to support different kinds of applications, ranging from security and access control applications to low enforcement applications. However, such systems raise serious privacy and data protection concerns. Misuse of data, compromising the privacy of individuals and/or authorized processing of data may be irreversible and could have severe consequences on the individual’s rights to privacy and data protection. This is partly due to the lack of methods and guidance for the integration of data protection and privacy by design in the system development process. In this paper, we present an example of privacy and data protection best practices to provide more guidance for data controllers and developers on how to comply with the legal obligation for data protection. These privacy and data protection best practices and considerations are based on the lessons learned from the SMart mobILity at the European land borders (SMILE) project.

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Notes

  1. 1.

    https://smile-h2020.eu/smile/.

  2. 2.

    The European Data Protection Board is an independent European body whose purpose is to ensure consistent application of the General Data Protection Regulation and to promote cooperation among the EU’s data protection authorities.

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Acknowledgements

This work is carried out in the EU-funded project SMILE (Project ID: 740931), [H2020-DS-2016-2017] SEC-14-BES-2016 towards reducing the cost of technologies in land border security applications.

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Correspondence to Mohamed Abomhara .

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Abomhara, M., Yayilgan, S.Y. (2021). An Example of Privacy and Data Protection Best Practices for Biometrics Data Processing in Border Control: Lesson Learned from SMILE. 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_29

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  • DOI: https://doi.org/10.1007/978-3-030-71711-7_29

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