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RESEARCH AREA

Secure & Privacy-friendly Information Processing

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.

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Most Recent Publications

Title Date Authors Meta
 2019
2019
 Apratim Bhattacharyya, Mario Fritz, Bernt Schiele
 International Conference on Representation Learning (ICLR)
 2019
WACV
2019
 Hosnieh Sattar, Gerard Pons-Moll, Mario Fritz
 IEEE Winter Conference on Applications of Computer Vision (WACV)
 2019
WWW
2019
 Yang Zhang
 The Web Conference 2019
 2019
NDSS
2019
 Inken Hagestedt, Yang Zhang, Mathias Humbert, Pascal Berrang, Tang Haixu, Wang XiaoFeng, Michael Backes
 26th Annual Network and Distributed System Security Symposium (NDSS 2019)
 2019
NDSS
2019
 Ahmed Salem, Yang Zhang, Mathias Humbert, Pascal Berrang, Mario Fritz, Michael Backes
 26th Annual Network and Distributed System Security Symposium (NDSS 2019)