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GDPR Compliant Data Processing and Privacy Preserving Technologies: A Literature Review on Notable Horizon 2020 Projects

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Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1410)


This paper presents a practical literature review focusing on privacy preserving technologies and organizational measures developed and proposed for GDPR-compliant data processing. Based on the selected Horizon 2020 projects, it identifies the substantial data processing and big data challenges relevant to data protection and privacy. Then, it visits the prominent privacy preserving technologies and organizational measures addressing these challenges. Finally, it analyzes the focus areas of the selected projects, identifies the solution they propose, draws quantitative conclusions, and asserts recommendations for future projects.


  • Privacy preserving technologies
  • Data protection
  • Information ethics
  • GDPR
  • Tech and ethics
  • Technical and organizational measures

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie ITN EJD “Law, Science and Technology Rights of Internet of Everything” grant agreement No 814177.

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  • DOI: 10.1007/978-3-030-87687-6_17
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  1. SPECIAL. Home. Accessed 30 Jan 2021

  2. Rizzo, A.: MHMD Project Presentation. In My Health My Data, 4 (2017).

  3. Custers, B., et al.: Lists of ethical, legal, societal and economic issues of big data technologies. SSRN Electron. J. 19 (2018).

  4. Markopoulos, I.: Industry specific requirements analysis, definition of the vertical E2E data marketplace functionality and use cases definition I, 11 (2020).

  5. European Big Data Value Association: Strategic Research and Innovation Agenda. European Big Data Value, 4(October), 66 (2017).

  6. Veeningen, M.: SODA - Scalable Oblivious Data Analytics. SODA Project (2020).

  7. Timan, T., Mann, Z. (eds.): Data protection in the era of artificial intelligence. In: Trends, Existing Solutions and Recommendations for Privacy-Preserving Technologies, pp. 7–8 (2019)

    Google Scholar 

  8. Budig, T., Herrmann, S., Dietz, A., Pandl Supervisor, K., Sunyaev, A. (n.d.): Trade-offs between Privacy-Preserving and Explainable Machine Learning in Healthcare, 5. Accessed 1 Feb 2021

  9. Domingo-Ferrer, J., Blanco-Justicia, A.: Privacy-preserving technologies. In: Christen, M., Gordijn, B., Loi, M. (eds.) The Ethics of Cybersecurity. TILELT, vol. 21, pp. 279–297. Springer, Cham (2020).

    CrossRef  Google Scholar 

  10. Allen, C.: The Path to Self-Sovereign Identity. Life With Alacrity (2016).

  11. Narayanan, A., Shmatikov, V.: Robust de-anonymization of large sparse datasets. In: Proceedings - IEEE Symposium on Security and Privacy, pp. 111–125 (2008).

  12. Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated Machine learning: concept and applications. In: ACM Transactions on Intelligent Systems and Technology, vol. 10, no. 12, p. 4 (2019).

  13. Truex, S., et al.: A hybrid approach to privacy-preserving federated learning. In: Proceedings of the ACM Conference on Computer and Communications Security pp. 1–11, 1 (2019).

  14. Pearson, S., Casassa-Mont, M.: Sticky policies: an approach for managing privacy across multiple parties. Computer 44(9), 60–68, (2011).

  15. Deborah Raji, I., et al.: Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing. In: ACM Reference Format, 1 (2020).

  16. Kassir, S. (n.d.): Algorithmic Auditing: The Key to Making Machine Learning in the Public Interest. The Business of Government, 1–4.

  17. Jobin, A., Ienca, M., Vayena, E.: Artificial Intelligence: The Global Landscape of Ethics Guidelines, 7. In arXiv (2019)

    Google Scholar 

  18. MHMD.: My Health My Data. In My Health My Data (2019).

  19. SMOOTH (n.d.): About Smooth Project. Accessed 20 Mar 2021

  20. BPR4GDPR (n.d.): Innovation Proposal. 48. Accessed 20 Mar 2021

  21. DEFeND (n.d.): What is the Defend Project - Defend Project. Accessed 20 Mar 2021

  22. MOSAICrOWN (n.d.): Homepage. Accessed 21 Mar 2021

  23. Yod, S.M.: PDP4E - D 2.4 Overall System Requirements (2019).

  24. Sartor, G., European U. I. of F.: The impact of the General Data Protection Regulation (GDPR) on artificial intelligence. In: Panel for the Future of Science and Technology (STOA), 1st, pp. 76–79 (2020).

  25. Yalçın, O.G.: Examination of current AI systems within the scope of right to explanation and designing explainable AI systems. In: CEUR Workshop Proceedings, pp. 2–3, 2598 (2020).

  26. GenoMed4All. About. Accessed 20 Mar 2021

  27. XAI. Research lines. Accessed 30 May 2021

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Correspondence to Orhan Gazi Yalcin .

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Yalcin, O.G. (2022). GDPR Compliant Data Processing and Privacy Preserving Technologies: A Literature Review on Notable Horizon 2020 Projects. In: de Paz Santana, J.F., de la Iglesia, D.H., López Rivero, A.J. (eds) New Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence. DiTTEt 2021. Advances in Intelligent Systems and Computing, vol 1410. Springer, Cham.

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