The Internet of today is a gigantic data storage that collects sensitive personal data about its users. This leads to significant security and privacy risks, as end users lose control over the data they share.
Developing methods and tools to enable a secure and privacy-friendly processing of data thus constitutes a core challenge to all data-driven ecosystems and applications. In particular, the success of digitalization heavily depends on whether companies are able to gain their users' trust regarding the protection of their privacy. This research area strives to develop disruptive new frameworks for reasoning about and improving security and privacy in information processing in various settings, efficiently and at scale. In the last years, this area has had a particular focus on the following topics: novel methods and tools for the algorithmic sanitization of privacy-sensitive data, in particular for genomic and medical research, new techniques for quantitatively assessing end user privacy, as well as empirical methods and tools for dealing with unstructured, heterogeneous datasets at scale.