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RESEARCH AREA

Trustworthy Information Processing

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Trustworthy 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
 2020
2020
 Seong Joon Oh, Rodrigo Benenson, Mario Fritz, Bernt Schiele
 NRA1
 IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
 2020
NDSS
2020
 Yang Zhang, Mathias Humbert, Bartlomiej Surma, Praveen Manoharan, Jilles Vreeken, Michael Backes
 NRA1
 Annual Network and Distributed System Security Symposium
 2020
USENIX-Security
2020
 Ahmed Salem, Apratim Bhattacharya, Michael Backes, Mario Fritz, Yang Zhang
 NRA1
 USENIX Security Symposium
 2019
2019
 Hans{-}Joachim Hof, Mario Fritz, Christoph Krauss, Oliver Wasenm\{u}ller
 NRA4 , NRA1
 2019
CCS
2019
 Florian Tramèr, Pascal Dupré, Rusak Gili, Giancarlo Pellegrino, Dan Boneh
 NRA3 , NRA1
 CCS 19