IEEE Consumer Electronics Magazine - July/August 2021 - 88

Physical Unclonable Function
CONCLUSION
In this article, multiple trusted-third-parties
Figure 6. TTPM offloading-Comparative
evaluation.
has similar rewards but with significantly lower
real execution times. To determine the tradeoff
between the average reward and the execution
time, we calculate the ratio of the normalized
average reward over the normalized real execution
time. The results show that the MLLL algorithm
with a low learning parameter value
represents the best tradeoff due to the significantly
lower real execution time while achieving
similar rewards when compared to the other
algorithms. The superiority of the MLLL algorithm
is further supported by its intrinsic characteristic
to converge to the best equilibrium.
Transaction Time Speedup With Multiple TTPs
The performance benefit associated with
introducing multiple TTPs over the one TTP protocol
proposed by Calhoun et al.,10 are reported
on here. Only the MLLL algorithm from the previous
section is used in the hardware validation
experiments to measure the speed-up, but the
performance of the other algorithms can be
extrapolated from the results reported above.
Our testbed is composed of five TTPs and nine
customers. The customers were configured to
continuously carry out the blinding process,
which effectively emulates a much larger set of
customers. The average transaction time, measured
from the time instant when a customer
selects a Master TTP to the time instant when
the customer receives the blinded tokens from
the Master TTP improves from approximately
9 s with one TTP to 3.7 s with five TTPs, i.e., a
speedup of approximately 2.43.
88
(mTTPs) are utilized within the PUF-Cash protocol
for improving performance and privacy. A
set of AI algorithms are evaluated to determine
their effectiveness in selecting an optimal subset
of Slave TTPs for offloading the task of exchanging
issued e-Cash tokens for blinded tokens during
value transfer operations requested by Alice.
The combination of AI algorithms and mTTPs
provides a significant performance benefit to
PUF-Cash, while simultaneously reducing the
Bank's ability to correlate Alice's issued tokens
with her corresponding blinded tokens. The latter
property increases the level of privacy for
Alice, making PUF-Cash an attractive solution for
implementing e-Cash systems that are private
and scale with system demand.
ACKNOWLEDGMENTS
The research of Mr. Fragkos and Dr. Tsiropoulou
was conducted as part of the NSF CRII1849739.
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