Study on the technical evaluation of de-identification procedures for personal data in the automotive sector

The aim of this study was to identify and evaluate different de-identification techniques that may be used in several mobility-related use cases. To do so, four use cases have been defined in accordance with a project pa
The aim of this study was to identify and evaluate different de-identification techniques that may be used in several mobility-related use cases. To do so, four use cases have been defined in accordance with a project partner that focused on the legal aspects of this project, as well as with the VDA/FAT working group. Each use case aims to create different legal and technical issues with regards to the data and information that are to be gathered, used and transferred in the specific scenario. Use cases should therefore differ in the type and frequency of data that is gathered as well as the level of privacy and the speed of computation that is needed for the data. Upon identifying use cases, a systematic literature review has been performed to identify suitable de-identification techniques to provide data privacy. Additionally, external databases have been considered as data that is expected to be anonymous might be reidentified through the combination of existing data with such external data.
For each case, requirements and possible attack scenarios were created to illustrate where exactly privacy-related issues could occur and how exactly such issues could impact data subjects, data processors or data controllers. Suitable de-identification techniques should be able to withstand these attack scenarios. Based on a series of additional criteria, de-identification techniques are then analyzed for each use case. Possible solutions are then discussed individually in chapters 6.1 - 6.2. It is evident that no one-size-fits-all approach to protect privacy in the mobility domain exists. While all techniques that are analyzed in detail in this report, e.g., homomorphic encryption, differential privacy, secure multiparty computation and federated learning, are able to successfully protect user privacy in certain instances, their overall effectiveness differs depending on the specifics of each use case.
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Metadaten
Author:Kai Rannenberg, Sebastian Pape, Frédéric Tronnier, Sascha Löbner
URN:urn:nbn:de:hebis:30:3-634132
DOI:http://dx.doi.org/10.21248/gups.63413
Publisher:Universitätsbibliothek Johann Christian Senckenberg
Place of publication:Frankfurt am Main
Document Type:Report
Language:English
Year of Completion:2021
Year of first Publication:2021
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2021/10/28
Tag:attack scenarios; automotive sector; comparison; de-identification; requirements analysis
Issue:May 14, 2021
Pagenumber:127
Note:
The authors are grateful to the Forschungsvereinigung Automobiltechnik e.V. (FAT e.V.) who funded this research. FAT e.V. is a department of the German Association of the Automotive Industry (German: Verband der Automobilindustrie e. V., VDA).
We are in particular grateful to FAT’s working group “AK 31 Elektronik und Software” who not only initiated this research but also contributed to this report by providing input and feedback in various meetings.
Institutes:Wirtschaftswissenschaften
Dewey Decimal Classification:004 Datenverarbeitung; Informatik
330 Wirtschaft
Sammlungen:Universitätspublikationen
Licence (German):License Logo Veröffentlichungsvertrag für Publikationen

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