Extracting Weak PUFs from Differential Nonlinearity of Digital-to-Analog Converters

摘要

Physical Unclonable Functions utilize random variations from manufacturing to generate unpredictable, yet repeat-able fingerprints of devices for usage in a hardware cryptographic context. Most often, they are dedicated electrical circuits in integrated devices and thus occupy additional space while only few implementations exploiting already existing hardware. In this work, we analyze the possibility to extract hardware unique fingerprints from the distinct differential nonlinearities of analog-to-digital converters, which are present in almost every system. The transfer curves from measuring a large set of low-cost analog-to-digital converters with 12 bit resolution are analyzed regarding their quality as system fingerprints. Additional postprocessing methods are investigated for further improvement of the uniqueness metrics. The fingerprints are optimized to a perfect inter-hamming distance of 50% and close-to-maximum entropy. These improvements come at the cost of a reduced number of extracted bits, yet the minimum achieved number of 440 bits is sufficient for secret key generation. By thresholding and consequently avoiding unstable positions in the extracted bit-strings, the intra-hamming distance of unprocessed transfer curves could be reduced to less than 4%. Further measurements over a large temperature range shows that the error rate due to temperature drift never exceeds 13%.