Data Privacy for Big Automotive Data

Abstract: In an age where data is becoming increasingly more valuable as itallows for data analysis and machine learning, big data has become ahot topic. With big data processing, analyses can be carried out onhuge amounts of user data. Although big data analysis has increasedthe ability to learn more about a population, it also carries a risk toindividual users’ privacy, as big data can contain or reveal unintendedpersonal information. With the growing capacity to store and processsuch big data, the need to provide meaningful privacy guarantees tousers 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 beenabled through privacy-preserving techniques, and what challengesarise when implementing such analyses in a real setting.Our main focus is on differential privacy, a privacy model whichprotects individuals’ privacy, while still allowing analysts to learn sta-tistical information about a population. In order to have access to realworld use cases, we have studied privacy-preserving big data analysisin the context of the automotive domain.