Energy efficient mining on a quantum-enabled blockchain using light

We outline a quantum-enabled blockchain architecture based on a consortium of quantum servers. The network is hybridised, utilising digital systems for sharing and processing classical information combined with a fibre--optic infrastructure and quantum devices for transmitting and processing quantum information. We deliver an energy efficient interactive mining protocol enacted between clients and servers which uses quantum information encoded in light and removes the need for trust in network infrastructure. Instead, clients on the network need only trust the transparent network code, and that their devices adhere to the rules of quantum physics. To demonstrate the energy efficiency of the mining protocol, we elaborate upon the results of two previous experiments (one performed over 1km of optical fibre) as applied to this work. Finally, we address some key vulnerabilities, explore open questions, and observe forward--compatibility with the quantum internet and quantum computing technologies.Comment: 25 pages, 5 figure

An overview of memristive cryptography

Smaller, smarter and faster edge devices in the Internet of things era demands secure data analysis and transmission under resource constraints of hardware architecture. Lightweight cryptography on edge hardware is an emerging topic that is essential to ensure data security in near-sensor computing systems such as mobiles, drones, smart cameras, and wearables. In this article, the current state of memristive cryptography is placed in the context of lightweight hardware cryptography. The paper provides a brief overview of the traditional hardware lightweight cryptography and cryptanalysis approaches. The contrast for memristive cryptography with respect to traditional approaches is evident through this article, and need to develop a more concrete approach to developing memristive cryptanalysis to test memristive cryptographic approaches is highlighted.Comment: European Physical Journal: Special Topics, Special Issue on "Memristor-based systems: Nonlinearity, dynamics and applicatio

Design Impedance Mismatch Physical Unclonable Functions for IoT Security

We propose a new design, Physical Unclonable Function (PUF) scheme, for the Internet of Things (IoT), which has been suffering from multiple-level security threats. As more and more objects interconnect on IoT networks, the identity of each thing is very important. To authenticate each object, we design an impedance mismatch PUF, which exploits random physical factors of the transmission line to generate a security unique private key. The characteristic impedance of the transmission line and signal transmission theory of the printed circuit board (PCB) are also analyzed in detail. To improve the reliability, current feedback amplifier (CFA) method is applied on the PUF. Finally, the proposed scheme is implemented and tested. The measure results show that impedance mismatch PUF provides better unpredictability and randomness

A DESIGN STRATEGY TO IMPROVE MACHINE LEARNING RESILIENCY OF PHYSICALLY UNCLONABLE FUNCTIONS USING MODULUS PROCESS

Physically unclonable functions (PUFs) are hardware security primitives that utilize non-reproducible manufacturing variations to provide device-specific challenge-response pairs (CRPs). Such primitives are desirable for applications such as communication and intellectual property protection. PUFs have been gaining considerable interest from both the academic and industrial communities because of their simplicity and stability. However, many recent studies have exposed PUFs to machine-learning (ML) modeling attacks. To improve the resilience of a system to general ML attacks instead of a specific ML technique, a common solution is to improve the complexity of the system. Structures, such as XOR-PUFs, can significantly increase the nonlinearity of PUFs to provide resilience against ML attacks. However, an increase in complexity often results in an increase in area and/or a decrease in reliability. This study proposes a lightweight ring oscillator (RO)-based PUFs using an additional modulus process to improve ML resiliency. The idea was to increase the complexity of the RO-PUF without significant hardware overhead by applying a modulus process to the outcomes from the RO frequency counter. We also present a thorough investigation of the design space to balance ML resiliency and other performance metrics such as reliability, uniqueness, and uniformity