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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
IEEE S&P
2020
 Rakibul Hasan, David Crandall, Mario Fritz, Apu Kapadia
 NRA1 , NRA5
 IEEE Symposium on Security and Privacy (S&P)
 2020
CCS
2020
 Dingfan Chen, Ning Yu, Yang Zhang, Mario Fritz
 NRA1
 ACM Conference on Computer and Communications Security (CCS)
 2020
CVPR
2020
 Apratim Bhattacharyya, Shweta Mahajan, Mario Fritz, Bernt Schiele, Stefan Roth
 NRA1
 IEEE Conference on Computer Vision and Pattern Recognition
 2020
2020
 Seong Joon Oh, Rodrigo Benenson, Mario Fritz, Bernt Schiele
 NRA1
 IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
 2020
2020
 Tribhuvanesh Orekondy, Bernt Schiele, Mario Fritz
 NRA1 , NRA3
 International Conference on Representation Learning (ICLR)