Background and summary of fellowship
Fundamental bounds of information processing systems provide the limit on theoretically possible achievable performances. For instance, in communications, the information-theoretic Shannon capacity describes the fundamental bound on what communication rate can be maximally achieved with vanishing error probability. This fundamental bound can be then used as a benchmark for the actual system design. It is therefore very valuable for the system design assessment of an actual system and the question of additional development work in the system design might be worth it or if a system change for further improvement would be a better strategy. In a privacy and security setting, the fundamental bounds describe what performances an adversary can achieve in the worst case. It therefore can be used to derive security or privacy guarantees which leads to security- or privacy-by-designs. Moreover, the proof of the fundamental bound often reveals what information-processing structure is the most promising strategy. It therefore often provides a deep understanding of information processing and guides towards efficient design structures. The results are often timeless and there are numerous interesting open problems that need to be solved.
In this project, we want to explore fundamental bounds for traditional communication scenarios, source coding setups, distributed-decision making, physical-layer security and privacy as well as statistical learning and data disclosure.