Enhancing Identifiability of PUFs with Built-in Compensation through Nonlinear Transformations

In this work, we propose a novel strategy to increase the identifiability of any PUF that uses the compensated measuring technique. The proposed strategy consists of applying certain Nonlinear Transformations before performing the comparisons of the parameters extracted from the entropy source to obtain the output bit. With this purpose, we apply this technique to different types of PUFs constructed using public datasets, as well as a PUF implemented by ourselves. The results obtained show a significant improvement in the Inter-HD of the PUF of up to 6 percentage points. Likewise, we also show that for some cases, it is possible to improve identifiability by up to two orders of magnitude.