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.
USENIX-Security
Proceedings of USENIX 2023USENIX 2023
SP
Proceedings of the 44rd IEEE Symposium on Security and Privacy44rd IEEE Symposium on Security and Privacy
FST&TCS
Proceedings of FSTTCS 2022,42nd IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science
NeurIPS
Advances in Neural Information Processing Systems 35 (NeurIPS 2022)NeurIPS
NeurIPS
Advances in Neural Information Processing Systems 35 (NeurIPS 2022)NeurIPS