(Organized in the context of the hiring process for the new cybersecurity professorships.)
One of the most interesting strong PUF constructions is the Bistable-Ring PUF (BR PUF), which is derived from the Möbius Ring-Oscillator. This is due to the lack of a correct mathematical model describing its internal behavior. Despite the absence of a precise model we will show that the BR PUF can be strongly PAC learned. Towards this, we will use deep results from the field of Spectral Analysis of Boolean Functions ( modern Percolation Theory).
We show that the surprising and very low Average-Sensitivity of a BR PUF is the crucial insight into its PAC learnability. Additionally, we will visualize our results through intensive practical experiments on real BR PUFs. Time permitting, we aim to discuss a very recent insight on the existence of a single highly influential variable for every BR PUF. I.e., every BR PUF of length n has a single variable approximating the given BR PUF with a constant error bounded away from½.