Response to the Public Consultation on the EDPB Guidelines 02/2026 on Anonymisation

This publication provides a response to the public consultation of the European Data Protection Board (EDPB) on the draft Guidelines 02/2026 on Anonymisation, Version 1.0, which were adopted on 07 July 2026. The response was submitted to the EDPB on 29 October 2026. The response was co-authored by a multi-institution, multi-disciplinary group of German-based academic researchers which specialize in cybersecurity and privacy enhancing systems, data protection law, AI regulation and research data management. With reference to the Guidelines, the response focuses on key areas. These include a framework for assessing the risk of anonymised data, definitions of terminology used within the Guidelines, the criteria for the technical analysis of anonymisation, as well as the interplay between the Guidelines and scientific research data. Within these areas, specific issues are addressed, from which the co-authors derive concrete recommendations for the EDPB and the Guidelines. These issues include, amongst others, the assessment of the risk of identification, the construction of a chain of entities for the means reasonably likely to be used, a broadening of the scope of the definition for given data, the unclear ‘treat them differently’ element of identification, the use of ‘No Inference’ as the primary criterion for the technical analysis, and the recognition of established practices for anonymisation in scientific research.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-29
DOI
https://doi.org/10.5281/zenodo.23809334
Primary Topic
Privacy-Preserving Technologies in Data
Type
article
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article

Response to the Public Consultation on the EDPB Guidelines 02/2026 on Anonymisation

Simon Hanisch, Stephanie von Maltzan, Shlomi Hod, Thilo Gottschalk et al.
Zenodo (CERN European Organization for Nuclear Research)
Privacy-Preserving Technologies in Data
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Response to the Public Consultation on the EDPB Guidelines 02/2026 on Anonymisation

Simon Hanisch, Stephanie von Maltzan, Shlomi Hod, Thilo Gottschalk, Jan Krasni, Thorsten Strufe, Kati Mozygemba, Jochem W. Rieger, Abhishek Kumar Mishra, Patrick Brunner
article en

Abstract

This publication provides a response to the public consultation of the European Data Protection Board (EDPB) on the draft Guidelines 02/2026 on Anonymisation, Version 1.0, which were adopted on 07 July 2026. The response was submitted to the EDPB on 29 October 2026. The response was co-authored by a multi-institution, multi-disciplinary group of German-based academic researchers which specialize in cybersecurity and privacy enhancing systems, data protection law, AI regulation and research data management. With reference to the Guidelines, the response focuses on key areas. These include a framework for assessing the risk of anonymised data, definitions of terminology used within the Guidelines, the criteria for the technical analysis of anonymisation, as well as the interplay between the Guidelines and scientific research data. Within these areas, specific issues are addressed, from which the co-authors derive concrete recommendations for the EDPB and the Guidelines. These issues include, amongst others, the assessment of the risk of identification, the construction of a chain of entities for the means reasonably likely to be used, a broadening of the scope of the definition for given data, the unclear ‘treat them differently’ element of identification, the use of ‘No Inference’ as the primary criterion for the technical analysis, and the recognition of established practices for anonymisation in scientific research.

Zenodo (CERN European Organization for Nuclear Research)
Karlsruhe Institute of Technology (DE), Carl von Ossietzky Universität Oldenburg (DE), FIZ Karlsruhe – Leibniz Institute for Information Infrastructure (DE), University of Bremen (DE), Maastricht University (NL), Technische Universität Berlin (DE), Technische Universität Dresden (DE)
Openalex Percentile: Top 13%
Privacy-Preserving Technologies in Data
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