Electronic money or e-Cash is becoming increasingly popular as the preferred strategy for making purchases, both on- and off-line. 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 paper, 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 (PUFs) for authentication and encryption operations between Alice, the Bank and multiple trusted third parties (mTTPs). 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.

Electronic money (e-money or e-Cash) is the digital representation of physical banknotes augmented by added use cases of online and remote payments. This paper presents a novel, anonymous e-money transaction protocol, built based on physical unclonable functions (PUFs), titled PUF-Cash. PUF-Cash preserves user anonymity while enabling both offline and online transaction capability. The PUF’s privacy-preserving property is leveraged to create blinded tokens for transaction anonymity while its hardware-based challenge–response pair authentication scheme provides a secure solution that is impervious to typical protocol attacks. The scheme is inspired from Chaum’s Digicash work in the 1980s and subsequent improvements. Unlike Chaum’s scheme, which relies on Rivest, Shamir and Adlemans’s (RSA’s) multiplicative homomorphic property to provide anonymity, the anonymity scheme proposed in this paper leverages the random and unique statistical properties of synthesized integrated circuits. PUF-Cash is implemented and demonstrated using a set of Xilinx Zynq Field Programmable Gate Arrays (FPGAs). Experimental results suggest that the hardware footprint of the solution is small, and the transaction rate is suitable for large-scale applications. An in-depth security analysis suggests that the solution possesses excellent statistical qualities in the generated authentication and encryption keys, and it is robust against a variety of attack vectors including model-building, impersonation, and side-channel variants.

Authenticated key exchange is desired in scenarios where two participants must exchange sensitive information over an untrusted channel but do not trust each other at the outset of the exchange. As a unique hardware-based random oracle, physical unclonable functions (PUFs) can embed cryptographic hardness and binding properties needed for a secure, interactive authentication system. In this paper, we propose a lightweight protocol, termed PUF-MAKE, to achieve bilateral mutual authentication between two untrusted parties with the help of a trusted server and secure physical devices. At the end of the protocol, both parties are authenticated and possess a shared session key that they can use to encrypt sensitive information over an untrusted channel. The PUF’s underlying entropy hardness characteristics and the key-encryption-key (KEK) primitive act as the root of trust in the protocol’s construction. Other salient properties include a lightweight construction with minimal information stored on each device, a key refresh mechanism to ensure a fresh key is used for every authentication, and robustness against a wide range of attacks. We evaluate the protocol on a set of three FPGAs and a desktop server, with the computational complexity calculated as a function of primitive operations. A composable security model is proposed and analyzed considering a powerful adversary in control of all communications channels. In particular, session key confidentiality is proven through formal verification of the protocol under strong attacker (Dolev-Yao) assumptions, rendering it viable for high-security applications such as digital currency.

Building on prior concepts of electronic money (eCash), we introduce a digital currency where a physical unclonable function (PUF) engenders devices with the twin properties of being verifiably enrolled as a member of a legitimate set of eCash devices and of possessing a hardware-based root-of-trust. A hardware-obfuscated secure enclave (HOSE) is proposed as a means of enabling a PUF-based propagation-of-provenance (POP) mechanism, which allows eCash tokens ( eCt ) to be securely signed and validated by recipients without incurring any third-party dependencies at transfer time. The POP scheme establishes a chain of custody starting with token creation, extending through multiple bilateral in-field transactions, and culminating in redemption at the token-issuing authority. A lightweight mutual-zero-trust (MZT) authentication protocol establishes a secure channel between any two fielded devices. The POP and MZT protocols, in combination with the HOSE, enable transitivity and anonymity of eCt transfers between online and offline devices.

Autonomous trust mechanisms enable Internet of Things (IoT) devices to function cooperatively in a wide range of ecosystems, from vehicle-to-vehicle communications to mesh sensor networks. A common property desired in such networks is a mechanism to construct a secure, authenticated channel between any two participating nodes to share sensitive information, nominally a challenging proposition for a large, heterogeneous network where node participation is constantly in flux. This work explores a contract-theoretic framework that exploits the principles of network economics to crowd-source trust between two arbitrary nodes based on the efforts of their neighbors. Each node in the network possesses a trust score, which is updated based on useful effort contributed to the authentication step. The scheme functions autonomously on locally adjacent nodes and is proven to converge onto an optimal solution based on the available nodes and their trust scores. Core building blocks include the use of Stochastic Learning Automata to select the participating nodes based on network and social metrics, and the formulation of a Bayesian trust belief distribution from the past behavior of the selected nodes. An effort-reward model incentivizes selected nodes to accurately report their trust scores and contribute their effort to the authentication process. Detailed numerical results obtained via simulation highlight the proposed framework’s efficacy and performance. The performance achieved near-optimal results despite incomplete information regarding the IoT nodes’ trust scores and the presence of malicious or misbehaving nodes. Comparison metrics demonstrate that the proposed approach maximized the overall social welfare and achieved better performance compared to the state of the art in the domain.