Physically unclonable functions are increasingly used as security primitives for device identification and anti-counterfeiting. However, PUFs are associated with noise and bias which in turn affects its property of reliability and predictability. The noise is corrected using fuzzy extractors, but the helper data generated during the process may cause leakage in min-entropy due to the bias observed in the response. This thesis offers two optimization techniques for PUF based protocols. The first part talks about the construction of a secure enrollment solution for PUFs on a low-end resource-constrained device using a microcontroller and a secure networked architecture. The second part deals with the combined optimization of min-entropy and error-rate using symbol clustering techniques to improve the reliability of SRAM PUFs. The results indicate an increase in min-entropy without much effect on the error rate but at the expense of PUF size. / Master of Science