Today's Internet can be seen as a huge data store that collects personal and sensitive data about its users. This leads to significant security and privacy risks for end users, who 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 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 efficient techniques for secure, verifiable computation.
Most Recent Publications
|A Hybrid Model for Identity Obfuscation by Face Replacement||2018||2018 Qianru Sun, Ayush Tewari, Weipeng Xu, Mario Fritz, Christian Theobalt, Bernt Schiele,||European Conference on Computer Vision|
|A Survey on Routing in Anonymous Communication Protocols||2018||2018 Fatemeh Shirazi, Milivoj Simeonovski, Muhammad Rizwan Asghar, Michael Backes, Claudia Díaz,|
|A4NT: Author Attribute Anonymity by Adversarial Training of Neural Machine Translation||2018||2018 Rakshith Shetty, Bernt Schiele, Mario Fritz,||27th USENIX Security Symposium (USENIX Security 18)|
|Accurate and Diverse Sampling of Sequences based on a “Best of Many” Sample Objective||2018||2018 Apratim Bhattacharyya, Bernt Schiele, Mario Fritz,||IEEE Conference on Computer Vision and Pattern Recognition (CVPR)|
|Anonymisierungsverfahren für genetische Daten||2018||2018 Ninja Marnau, Pascal Berrang, Mathias Humbert,|