Data Privacy for Big Automotive Data
In an age where data is becoming increasingly more valuable as it
allows for data analysis and machine learning, big data has become a
hot topic. With big data processing, analyses can be carried out on
huge amounts of user data. Although big data analysis has increased
the ability to learn more about a population, it also carries a risk to
individual users’ privacy, as big data can contain or reveal unintended
personal information. With the growing capacity to store and process
such big data, the need to provide meaningful privacy guarantees to
users thus becomes a pressing issue.
We believe that techniques for privacy-preserving data analysis en-
ables big data analysis, by minimizing the privacy risk for individuals.
In this work we have further explored how big data analysis can be
enabled through privacy-preserving techniques, and what challenges
arise when implementing such analyses in a real setting.
Our main focus is on differential privacy, a privacy model which
protects individuals’ privacy, while still allowing analysts to learn sta-
tistical information about a population. In order to have access to real
world use cases, we have studied privacy-preserving big data analysis
in the context of the automotive domain.