Introducing Differential Privacy to the Automotive Domain: Opportunities and Challenges
Paper in proceeding, 2017

Privacy research is attracting increasingly more attention, especially with the upcoming general data protection regulation (GDPR) which will impose stricter rules on storing and managing personally identifiable information (PII) in Europe. For vehicle manufacturers, gathering data from connected vehicles presents new analytic opportunities, but if the data also contains PII, the data comes at a higher price when it must either be properly de-identified or gathered with contracted consent from the drivers. One option is to establish contracts with every driver, but the more tempting alternative is to simply de-identify data before it is gathered, to avoid handling PII altogether. However, several real-world examples have previously shown cases where re-identification of supposedly anonymized data was possible, and it has also been pointed out that PII has no technical meaning. Additionally, in some cases the manufacturer might want to release statistics either publicly or to an original equipment manufacturer (OEM). Given the challenges with properly de-identifying data, structured methods for performing de-identification should be used, rather than arbitrary removal of attributes believed to be sensitive. A promising research area to help mitigate the re-identification problem is differential privacy, a privacy model that unlike most privacy models gives mathematically rigorous privacy guarantees. Although the research interest is large, the amount of real-world implementations is still small, since understanding differential privacy and being able to implement it correctly is not trivial. Therefore, in this position paper, we set out to answer the questions of how and when to use differential privacy in the automotive industry, in order to bridge the gap between theory and practice. Furthermore, we elaborate on the challenges of using differential privacy in the automotive industry, and conclude with our recommendations for moving forward.


Boel Nelson

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Tomas Olovsson

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

IEEE Vehicular Technology Conference

15502252 (ISSN)

Vol. 2017-September 1-7

2017 IEEE 86th Vehicular Technology Conference (VTC-Fall),
Toronto, ON, Canada,

Areas of Advance

Information and Communication Technology


Subject Categories

Computer and Information Science



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