Enhancing Privacy in PUF-Cash through Multiple Trusted Third Parties and Reinforcement Learning

Electronic cash ( e-Cash ) is a digital alternative to physical currency such as coins and bank notes. Suitably constructed, e-Cash has the ability to offer an anonymous offline experience much akin to cash, and in direct contrast to traditional forms of payment such as credit and debit cards. Implementing security and privacy within e-Cash, i.e., preserving user anonymity while preventing counterfeiting, fraud, and double spending, is a non-trivial challenge. In this article, we propose major improvements to an e-Cash protocol, termed PUF-Cash, based on physical unclonable functions ( PUFs ). PUF-Cash was created as an offline-first, secure e-Cash scheme that preserved user anonymity in payments. In addition, PUF-Cash supports remote payments; an improvement over traditional currency. In this work, a novel multi-trusted-third-party exchange scheme is introduced, which is responsible for “blinding” Alice’s e-Cash tokens; a feature at the heart of preserving her anonymity. The exchange operations are governed by machine learning techniques which are uniquely applied to optimize user privacy, while remaining resistant to identity-revealing attacks by adversaries and trusted authorities. Federation of the single trusted third party into multiple entities distributes the workload, thereby improving performance and resiliency within the e-Cash system architecture. Experimental results indicate that improvements to PUF-Cash enhance user privacy and scalability.

Investigating the Antecedents and Role of Usage Fatigue on Online Commerce Usage Decrease

The internet has made the digital ecosystem a viable commerce destination, but has also made it a content and media dystopia. Illegal access to user information and the frequent data breaches have sparked interest in privacy protection behaviour. Privacy focused technologies have emerged to provide personalized privacy assurance. It is as if a digital civil war is underway, and the online commerce industry is the collateral damage. The study investigates the antecedents and influence of usage fatigue on online commerce usage decrease. Structural equation modelling is used to analyze responses from three higher education institutions in Cyprus. Five hundred eighty-seven responses are drawn and used for the analysis. User anonymity, perceived effectiveness of industry-self regulation, and privacy literacy are the main antecedents of usage fatigue. Usage fatigue positively influences online commerce usage decrease and is more apparent among with people higher perception of industry self-regulation, an indication of low effectiveness of industry self-regulation of online commerce in the region.

Zero-Knowledge Proof Based Authentication Over Untrusted Networks

Zero knowledge proof is a powerful cryptographic protocol that is utilized to establish data security whilst ensuring and maintaining user anonymity. ZKP has relatively less complex computational requirements as compared to the other protocols for authentication. Conventional authentication schemes are susceptible to attacks such as MiTM, IP spoofing, DoS, replay and other eavesdropping based attacks, when the data is shared across an untrusted network. This paper shows an approach to ensure authentication of a device over an untrusted network whilst maintaining and safeguarding user credentials, by using the concepts of ZKP protocol.