Incorporating privacy by design in Body Sensor Networks for Medical Applications: A Privacy and Data Protection Framework

Christos Kalloniatis1, Costas Lambrinoudakis1, Mathias Musahl2, Athanasios Kanatas1 and Stefanos Gritzalis1

  1. Dept. of Digital Systems, University of Piraeus
    GR 18532, Piraeus, Greece
    {chkallon,clam,kanatas,sgritz}@unipi.gr
  2. German Research Center for Artificial Intelligence
    67663 Kaiserslautern, Germany
    mathias.musahl@dfki.de

Abstract

Privacy and Data protection are highly complex issues within eHealth/M-Health systems. These systems should meet specific requirements deriving from the organizations and users, as well as from the variety of legal obligations deriving from GDPR that dictate protection rights of data subjects and responsibilities of data controllers. To address that, this paper proposes a Privacy and Data Protection Framework that provides the appropriate steps so as the proper technical, organizational and procedural measures to be undertaken. The framework, beyond previous literature, supports the combination of privacy by design principles with the newly introduced GDPR requirements in order to create a strong elicitation process for deriving the set of the technical security and privacy requirements that should be addressed. It also proposes a process for validating that the elicited requirements are indeed fulfilling the objectives addressed during the Data Protection Impact Assessment (DPIA), carried out according to the GDPR.

Key words

privacy protection, data protection, GDPR, Framework

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS200922057K

Publication information

Volume 18, Issue 1 (January 2021)
Year of Publication: 2021
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

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How to cite

Kalloniatis, C., Lambrinoudakis, C., Musahl, M., Kanatas, A., Gritzalis, S.: Incorporating privacy by design in Body Sensor Networks for Medical Applications: A Privacy and Data Protection Framework. Computer Science and Information Systems, Vol. 18, No. 1, 323–350. (2021), https://doi.org/10.2298/CSIS200922057K