Keywords: data science, privacy preservation, personal data, federated learning

"Privacy-preserving personal data analysis"

An ever-increasing amount of data is generated by our citizens and used in our daily life every single day. These massive amounts of data can be used to improve digital technologies and develop data-driven innovations that can impact every aspect of people’s lives. However, a lack of sharing of, access to, and reuse of data from multiple organizations hinders the analysis possibilities and hence potential insights from the data. Several challenges have been recognized, such as technical barriers, security, data-protection compliance to one or more legal jurisdictions, privacy concerns, and trust issues.

This thesis aims to develop new privacy-preserving data sharing and analysis techniques that strengthen and extend the (re-)use of personal data while maximally protecting individuals’ privacy. To achieve this aim, this thesis addressed the research challenges of personal data sharing and -use from the perspectives of data organizations, scientific researchers, and individuals (citizens).

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Language: English