Comparison of De-Identification Techniques for Privacy Preserving Data Analysis in Vehicular Data Sharing-Reference-Cited by-同舟云学术

Comparison of De-Identification Techniques for Privacy Preserving Data Analysis in Vehicular Data Sharing

Published:2021-11-30 Issue: Volume: Page:
ISSN:
Container-title:Computer Science in Cars Symposium
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Author:

Löbner Sascha¹,Tronnier Frédéric²,Pape Sebastian²,Rannenberg Kai³

Affiliation:

1. Chair of Mobile Business and Multilateral Security, Goethe-University Frankfurt, DE

2. Goethe-University Frankfurt, DE

3. Goethe-University Frankfurt, Germany

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3488904.3493380

Reference45 articles.

1. 2014. Consumer Privacy Protection Principles – PRIVACY PRINCIPLES FOR VEHICLE TECHNOLOGIES AND SERVICES. https://cryptome.org/2014/11/auto-privacy-principles.pdf 2014. Consumer Privacy Protection Principles – PRIVACY PRINCIPLES FOR VEHICLE TECHNOLOGIES AND SERVICES. https://cryptome.org/2014/11/auto-privacy-principles.pdf

2. 2017. Vehicle Data Privacy – Industry and Federal Efforts Under Way but NHTSA Needs to Define Its Role. https://www.gao.gov/assets/gao-17-656.pdf 2017. Vehicle Data Privacy – Industry and Federal Efforts Under Way but NHTSA Needs to Define Its Role. https://www.gao.gov/assets/gao-17-656.pdf

3. 2020. Guidelines 1/2020 on processing personal data in the context of connected vehicles and mobility related applications. https://edpb.europa.eu/sites/default/files/consultation/edpb_guidelines_202001_connectedvehicles.pdf 2020. Guidelines 1/2020 on processing personal data in the context of connected vehicles and mobility related applications. https://edpb.europa.eu/sites/default/files/consultation/edpb_guidelines_202001_connectedvehicles.pdf

4. Privacy-Preserving Machine Learning: Threats and Solutions

5. Geo-indistinguishability

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