Longterm-secure cryptographic methods
We are working on the development of new cryptographic methods that have already convincingly shown that they are also resistant to the threat of quantum computing and can be used as privacy-enhancing technologies (PETs). Concrete further goals are the design and security analysis of new secure post-quantum methods that are secure against both classical and quantum attacks.
Privacy-Preserving Data Analytics
We aim to advance the state-of-the-art for privacy-compatible methods such as HE, MPC, or PSI and overcome performance issues typically encountered in more complex evaluations such as machine learning applications. We are developing long-term secure cryptographic systems that can be used for privacy-compatible data analysis, especially in the context of machine learning and AI.
We bring together our expertise in cryptography and analytics with quantum information and quantum algorithms. Given recent breakthroughs in the development of quantum computers, the scenario of quantum computing is becoming increasingly realistic. However, because users of quantum computers have to share their data with operators, many questions about privacy and data security are raised.