Anonymity Assessment – A Universal Tool for Measuring Anonymity of Data Sets Under the GDPR with a Special Focus on Smart Robotics

30 Pages Posted: 11 Feb 2022

See all articles by Michael Kolain

Michael Kolain

German Research Institute for Public Administration (FÖV Speyer)

Christian Grafenauer

affiliation not provided to SSRN

Martin Ebers

Humboldt University of Berlin - Faculty of Law; University of Tartu, School of Law

Date Written: November 24, 2021

Abstract

As soon as personal data is processed, European data protection law (esp. the GDPR) provides very strict rules that must be observed by data controllers and processors. This leads to a variety of problems, especially in the case of artificial intelligence (AI) systems and smart robotics, as the GDPR takes these new technologies insufficiently into account. By introducing the method of “Anonymity Assessment,” we propose an interdisciplinary approach to classifying anonymity and measuring the degree of pseudo-anonymization of a given data set in a legal and technical sense. The legal provisions of GDPR are therefore “translated” into mathematical equations. To this end, we propose two scores: the Objective Anonymity Score (OAS), which determines the risk of (re-)identifying a natural person under objective statistical measures; and the Subjective Anonymity Score (SAS), which takes into account the subjective perspective of data controllers or processors.

Keywords: anonymity, anonymization, GDPR, data protection, re-identification, Anonymity Assessment, anonymous data, PII, personal data, pseudonymous data, GDPR, Artificial Intelligence, training data, smart robotics, risk-based approach, data privacy, technical measures, anonymity score

Suggested Citation

Kolain, Michael and Grafenauer, Christian and Ebers, Martin, Anonymity Assessment – A Universal Tool for Measuring Anonymity of Data Sets Under the GDPR with a Special Focus on Smart Robotics (November 24, 2021). Rutgers University Computer & Technology Law Journal, Vol. 48, No. 2, 2022, Available at SSRN: https://ssrn.com/abstract=3971139

Michael Kolain

German Research Institute for Public Administration (FÖV Speyer) ( email )

Freiherr-vom-Stein-Str. 2
Speyer
Germany

HOME PAGE: http://https://www.foev-speyer.de/forschung/digitalisierung-1

Christian Grafenauer (Contact Author)

affiliation not provided to SSRN

Martin Ebers

Humboldt University of Berlin - Faculty of Law ( email )

Unter den Linden 6
Berlin, D-10099
Germany

University of Tartu, School of Law ( email )

Tartu
Estonia

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