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Paper 1

Enhancing Multi-Factor Authentication with Templateless 2D/3D Biometrics and PUF Integration for Securing Smart Devices

Saloni Jain, Amisha Bagri, Maxime Cambou, Dina Ghanai Miandoab, Bertrand Cambou

Year

2025

Journal

Cryptography

DOI

10.3390/cryptography9040068

arXiv

-

Secure authentication in smart device ecosystems remains a critical challenge, particularly due to the irrevocability of compromised biometric templates in server-based systems. This paper presents a post-quantum secure multi-factor authentication protocol that combines templateless 2D and 3D facial biometrics, liveness detection, and Physical Unclonable Functions (PUFs) to achieve robust identity assurance. The protocol exhibits zero-knowledge properties, preventing adversaries from identifying whether authentication failure is due to the biometric, password, PUF, or liveness factor. The proposed protocol utilizes advanced facial landmark detection via dlib or mediapipe, capturing multi-angle facial data and mapping it. By applying a double-masking technique and measuring distances between randomized points, stabilized facial landmarks are selected through multiple images captured during enrollment to ensure template stability. The protocol creates high-entropy cryptographic keys, securely erasing all raw biometric data and sensitive keys immediately after processing. All key cryptographic operations and challenge-response exchanges employ post-quantum algorithms, providing resistance to both classical and quantum adversaries. To further enhance reliability, advanced error-correction methods mitigate noise in biometric and PUF responses, resulting in minimal FAR and FRR that meets industrial standards and resilience against spoofing. Our experimental results demonstrate this protocol’s suitability for smart devices and IoT deployments requiring high-assurance, scalable, and quantum-resistant authentication.

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Paper 2

QKAN: Quantum Kolmogorov-Arnold Networks

Petr Ivashkov, Po-Wei Huang, Kelvin Koor, Lirandë Pira, Patrick Rebentrost

Year

2024

Journal

arXiv preprint

DOI

arXiv:2410.04435

arXiv

2410.04435

The potential of learning models in quantum hardware remains an open question. Yet, the field of quantum machine learning persistently explores how these models can take advantage of quantum implementations. Recently, a new neural network architecture, called Kolmogorov-Arnold Networks (KAN), has emerged, inspired by the compositional structure of the Kolmogorov-Arnold representation theorem. In this work, we design a quantum version of KAN called QKAN. Our QKAN exploits powerful quantum linear algebra tools, including quantum singular value transformation, to apply parameterized activation functions on the edges of the network. QKAN is based on block-encodings, making it inherently suitable for direct quantum input. Furthermore, we analyze its asymptotic complexity, building recursively from a single layer to an end-to-end neural architecture. The gate complexity of QKAN scales linearly with the cost of constructing block-encodings for input and weights, suggesting broad applicability in tasks with high-dimensional input. QKAN serves as a trainable quantum machine learning model by combining parameterized quantum circuits with established quantum subroutines. Lastly, we propose a multivariate state preparation strategy based on the construction of the QKAN architecture.

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