This special session paper discusses recent advances on photonic physical unclonable functions (PUFs), providing a broader overview of and motivation for photonic PUFs. We discuss their potential advantages, such as a higher entropy, larger complexity, and possibly better resilience against machine learning attacks. We also deal with some recent implementations based on linear and non-linear optics, alongside with their main advantages and limitations.
This special session paper discusses recent advances on photonic physical unclonable functions (PUFs), providing a broader overview of and motivation for photonic PUFs. We discuss their potential advantages, such as a higher entropy, larger complexity, and possibly better resilience against machine learning attacks. We also deal with some recent implementations based on linear and non-linear optics, alongside with their main advantages and limitations.
With the accelerating expansion of connectivity, the need for advanced cyber-physical security technologies that bridge the digital and physical worlds is becoming more crucial than ever. Physically unclonable functions (PUFs) leveraging nanotechnologies and photonic technologies are emerging as practical and deployable hardware security solutions that go beyond software-based hardware security. Optics- and photonics-based PUFs (often referred to as optical PUFs) offer a range of characteristics beneficial to multiplex strategies that incorporate multilevel and multimodal approaches, based on their fundamental optical and photonic properties. PUFs are categorized as multilevel when multiple responses are generated from a single type of challenge, such as a light source or optical tool. In contrast, they are classified as multimodal when multiple responses are created from various types of challenges involving a combination of different light sources and optical tools. This review highlights recent advances and progress in integrating emerging materials, detection tools, authentication methods, imaging techniques, reconfigurability, and unclonability, which are essential for advancing next-generation multiplex PUFs. To facilitate real-world applications and to support deployment in real-world senarios, we also discuss existing limitations in multiplex optical PUFs and explore potential strategies to overcome such challenges, supporting our increasingly hyperconnected society.
Physically unclonable functions (PUFs) have emerged as a promising hardware security primitive, and recent years have seen growing interest in realizing PUFs on photonic integrated circuits (PICs). We classify existing PIC-based PUFs into electrically configured and optically encoded designs. Notably, security properties remain insufficiently analyzed, with mathematical unclonability often assumed without supporting argument. To illustrate this issue, we introduce a modeling attack against the subclass of optically encoded PUFs that operate in a linear optical regime. We show that, for a representative simulated PUF construction, observing as few as 200 challenge-response pairs allows learning the challenge-response mapping. Under canonical modeling assumptions, linear optically encoded PUFs are therefore mathematically clonable, calling for a careful assessment of their suitability for authentication protocols that rely on mathematical unclonability.
The hallmark of the information age is the ease with which information is stored, accessed, and shared throughout the globe. This is enabled, in large part, by the simplicity of duplicating digital information without error. Unfortunately, an ever-growing consequence is the global threat to security and privacy enabled by our digital reliance. Specifically, modern secure communications and authentication suffer from formidable threats arising from the potential for copying of secret keys stored in digital media. With relatively little transfer of information, an attacker can impersonate a legitimate user, publish malicious software that is automatically accepted as safe by millions of computers, or eavesdrop on countless digital exchanges. To address this vulnerability, a new class of cryptographic devices known as physical unclonable functions (PUFs) are being developed. PUFs are modern realizations of an ancient concept, the physical key, and offer an attractive alternative for digital key storage. A user derives a digital key from the PUF’s physical behavior, which is sensitive to physical idiosyncrasies that are beyond fabrication tolerances. Thus, unlike conventional physical keys, a PUF cannot be duplicated and only the holder can extract the digital key. However, emerging machine learning (ML) methods are remarkably adept at learning behavior via training, and if such algorithms can learn to emulate a PUF, then the security is compromised. Unfortunately, such attacks are highly successful against conventional electronic PUFs. Here, we investigate ML attacks against a nonlinear silicon photonic PUF, a novel design that leverages nonlinear optical interactions in chaotic silicon microcavities. First, we investigate these devices’ resistance to cloning during fabrication and demonstrate their use as a source of large volumes of cryptographic key material. Next, we demonstrate that silicon photonic PUFs exhibit resistance to state-of-the-art ML attacks due to their nonlinearity and finally validate this resistance in an encryption scenario.
Physical Unclonable Functions (PUFs) have garnered significant attention for their potential in enhancing the security of IoT devices, offering an alternative to traditional computational-based approaches. Despite their potential, classical PUFs suffer from critical drawbacks, including vulnerability to machine learning attacks and inadequate defense against quantum adversaries. To address these limitations, alternative architectures utilizing quantum mechanical principles, known as Quantum PUFs (QPUFs), have been proposed. This paper presents a comprehensive survey of the diverse range of QPUFs, detailing their architectures and security features. Additionally, it explores the information-theoretic applications of QPUFs in IoT, including secret key generation, secure storage, authentication, and identification. The findings indicate that classical results translate effectively to the quantum domain in most cases, with QPUFs exhibiting larger capacity regions compared to their classical counterparts.
A Physical Unclonable Function (PUF) is a device with unique behaviour that is hard to clone hence providing a secure fingerprint. A variety of PUF structures and PUF-based applications have been explored theoretically as well as being implemented in practical settings. Recently, the inherent unclonability of quantum states has been exploited to derive the quantum analogue of PUF as well as new proposals for the implementation of PUF. We present the first comprehensive study of quantum Physical Unclonable Functions (qPUFs) with quantum cryptographic tools. We formally define qPUFs, encapsulating all requirements of classical PUFs as well as introducing a new testability feature inherent to the quantum setting only. We use a quantum game-based framework to define different levels of security for qPUFs: quantum exponential unforgeability, quantum existential unforgeability and quantum selective unforgeability. We introduce a new quantum attack technique based on the universal quantum emulator algorithm of Marvin and Lloyd to prove no qPUF can provide quantum existential unforgeability. On the other hand, we prove that a large family of qPUFs (called unitary PUFs) can provide quantum selective unforgeability which is the desired level of security for most PUF-based applications.