TY - GEN
T1 - Exploring privacy-enhancing technologies in the automotive value chain
AU - Garrido, Gonzalo Munilla
AU - Schmidt, Kaja
AU - Harth-Kitzerow, Christopher
AU - Klepsch, Johannes
AU - Luckow, Andre
AU - Matthes, Florian
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Privacy-enhancing technologies (PETs) are becoming increasingly crucial for addressing customer needs, security, privacy (e. g., enhancing anonymity and confidentiality), and regulatory requirements. However, applying PETs in organizations requires a precise understanding of use cases, technologies, and limitations. This paper investigates several industrial use cases, their characteristics, and the potential applicability of PETs to these. We conduct expert interviews to identify and classify uses cases, a gray literature review of relevant open-source PET tools, and discuss how the use case characteristics can be addressed using PETs' capabilities. While we focus mainly on automotive use cases, the results also apply to other use case domains.
AB - Privacy-enhancing technologies (PETs) are becoming increasingly crucial for addressing customer needs, security, privacy (e. g., enhancing anonymity and confidentiality), and regulatory requirements. However, applying PETs in organizations requires a precise understanding of use cases, technologies, and limitations. This paper investigates several industrial use cases, their characteristics, and the potential applicability of PETs to these. We conduct expert interviews to identify and classify uses cases, a gray literature review of relevant open-source PET tools, and discuss how the use case characteristics can be addressed using PETs' capabilities. While we focus mainly on automotive use cases, the results also apply to other use case domains.
KW - Privacy-enhancing technologies (PETs)
KW - anonymization
KW - applications
KW - automotive
KW - confidentiality
UR - https://www.scopus.com/pages/publications/85125362708
U2 - 10.1109/BigData52589.2021.9671528
DO - 10.1109/BigData52589.2021.9671528
M3 - Conference contribution
AN - SCOPUS:85125362708
T3 - Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
SP - 1265
EP - 1272
BT - Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
A2 - Chen, Yixin
A2 - Ludwig, Heiko
A2 - Tu, Yicheng
A2 - Fayyad, Usama
A2 - Zhu, Xingquan
A2 - Hu, Xiaohua Tony
A2 - Byna, Suren
A2 - Liu, Xiong
A2 - Zhang, Jianping
A2 - Pan, Shirui
A2 - Papalexakis, Vagelis
A2 - Wang, Jianwu
A2 - Cuzzocrea, Alfredo
A2 - Ordonez, Carlos
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Big Data, Big Data 2021
Y2 - 15 December 2021 through 18 December 2021
ER -