Reinforcement Learning Toward Decision-Making for Multiple Trusted-Third-Parties in PUF-Cash

Abstract: Electronic money is the digital representation of physical banknotes enabling offline and online payments. An electronic e-Cash scheme, termed PUF- Cash was proposed in prior work. PUF-Cash preserves user anonymity by leveraging the random and unique statistical properties of physically unclonable functions (PUFs). PUF-Cash is extended meaningfully in this work by the introduction of multiple trusted third parties (TTPs) for token blinding and a fractional scheme to diversify and mask Alice's spending habits from the Bank. A reinforcement learning (RL) framework based on stochastic learning automata (SLA) is proposed to efficiently select a subset of TTPs as well as the fractional amounts for blinding per TTP, based on the set of available TTPs, the computational load per TTP and network conditions. An experimental model was constructed in MATLAB with multiple TTPs to verify the learning framework. Results indicate that the RL approach guarantees fast convergence to an efficient selection of TTPs and allocation of fractional amounts in terms of perceived reward for the end-users.