Description
Abstract: Electronic money or e-Cash is becoming increasingly popular as the preferred strategy for making purchases, both on and offline. Several unique attributes of e-Cash are appealing to customers, including the convenience of always having cash-on-hand without the need to periodically visit the ATM, the ability to perform peer-to-peer transactions without an intermediary, and the peace of mind associated in conducting those transactions privately. Equally important is that paper money provides customers with an anonymous method of payment, which is highly valued by many individuals. Although anonymity is implicit with fiat money, it is a difficult property to preserve within e-Cash schemes. In this article, we investigate several artificial intelligence (AI) approaches for improving performance and privacy within a previously proposed e-Cash scheme called PUF-Cash.PUF-Cash utilizes physical unclonable functions for authentication and encryption operations between Alice, the Bank, and multiple trusted third parties. The AI methods select a subset of the TTPs and distribute withdrawal amounts to maximize the performance and privacy associated with Alice's e-Cash tokens. Simulation results show the effectiveness of the various AI approaches using a large test-bed architecture.