Physical Unclonable Functions (PUFs) are an emerging security primitive useful for secure key storage mechanisms and anti-counterfeiting measures. To do this securely, it is imperative that PUFs are unique, i.e., possess enough entropy. The aim of this thesis is to develop and implement new methodologies to accurately estimate the entropy of PUFs. To this end, a novel method is presented which estimates the extractable entropy by calculating the mutual information between enrollment and reconstruction measurements. Furthermore, a method to determine uniqueness from the field of biometrics is modified in such a way that the entropy of PUFs can be estimated. Our newly developed entropy estimation methods are compared to other methods found in...