1 research outputs found
A statistical noise model for a class of Physically Unclonable Functions
The interest in "Physically Unclonable Function"-devices has increased
rapidly over the last few years, as they have several interesting properties
for system security related applications like, for example, the management of
cryptographic keys. Unfortunately, the output provided by these devices is
noisy and needs to be corrected for these applications.
Related error correcting mechanisms are typically constructed on the basis of
an equal error probability for each output bit. This assumption does not hold
for Physically Unclonable Functions, where varying error probabilities can be
observed. This results in a generalized binomial distribution for the number of
errors in the output.
The intention of this paper is to discuss a novel Bayesian statistical model
for the noise of an especially wide-spread class of Physically Unclonable
Functions, which properly handles the varying output stability and also
reflects the different noise behaviors observed in a collection of such
devices. Furthermore, we compare several different methods for estimating the
model parameters and apply the proposed model to concrete measurements obtained
within the CODES research project in order to evaluate typical correction and
stabilization approaches