Data Privacy for Big Automotive Data
Licentiatavhandling, 2017

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.

data privacy

differential privacy

big data

vehicular data


EE, Rännvägen 6, Chalmers
Opponent: Professor Vicenç Torra, University of Skövde, Sweden


Boel Nelson

Chalmers, Data- och informationsteknik, Nätverk och system

Introducing Differential Privacy to the Automotive Domain: Opportunities and Challenges

IEEE Vehicular Technology Conference,; Vol. 2017-September(2017)p. 1-7

Paper i proceeding

Security and Privacy for Big Data: A Systematic Literature Review

2016 IEEE International Conference on Big Data (Big Data),; (2016)p. 3693-3702

Paper i proceeding

Joint Subjective and Objective Data Capture and Analytics for Automotive Applications

2017 IEEE 86th Vehicular Technology Conference (VTC-Fall),; (2018)

Paper i proceeding

Nelson, B., Olovsson, T. LDPModE: Modular Software for Differentially Private Data Collection


Informations- och kommunikationsteknik



Data- och informationsvetenskap



EE, Rännvägen 6, Chalmers

Opponent: Professor Vicenç Torra, University of Skövde, Sweden

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