  The remaining min-entropy of a secret generated by fuzzy extraction from a
Physical Unclonable Function is typically estimated under the assumption of
independent and identically distributed PUF responses, but this assumption does
not hold in practice. This work analyzes the more realistic case that the
responses are independent but not necessarily identically distributed. For this
case, we extend the (n-k) bound and a tighter bound by Delvaux et al. In
particular, we suggest a grouping bound which provides a trade off for accuracy
vs computational effort. Comparison to previous bounds shows the accuracy and
efficiency of our bound. We also adapt the key rank (a tool from side-channel
analysis) to cross-validate the state-of-the-art and our proposed min-entropy
bounds based on publicly available PUF data from real hardware.
