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.

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.

Cryptocurrency wallets are widely used to manage digital assets and interact with NFT marketplaces. However, while blockchain can verify ownership and transactions, protecting the digital content associated with an NFT before and during its exchange remains challenging. This paper presents virtual PUF-based NFT marketplace integrated with software wallet to provide a secure content exchange workflow without relying on permanently stored secret material. The proposed approach encrypts the seller’s digital asset before listing and generates a content-bound preview that allows buyers to inspect the asset without revealing the complete content. A virtual PUF-based mechanism is used together with cryptographic commitments and buyer-specific, post quantum ML-KEM-768 key encapsulation to support secure verification and recovery. Smart contracts coordinate the listing, payment, release, and settlement process, while encrypted artifacts are stored off-chain. Security analysis and prototype evaluation show that the system protects against common attacks such as artifact tampering, underpayment, unauthorized release, and invalid key registration while introducing only modest runtime and storage overhead. This architecture effectively enhances security and access control for NFT-linked content within software wallets.

Physical Unclonable Functions (PUFs) authenticate a device by its manufacturing variation. However, the cryptographic layer only verifies that a reading matches the enrolled fingerprint. It does not confirm that a live, honest device produced that reading. As a result, replayed readings, cloned silicon, and fault injection can all pass verification. We benchmarked our deployed machine-learning detector, UnifiedPUFNet, against six established architectures on 1~Mbit readings from 24 static random-access memory (SRAM) PUF tokens, withholding one attack family from training at a time. Six of the seven architectures reached an area under the receiver operating characteristic curve (AUC) of at least 0.999 in distribution, yet all of them degraded on attack families absent from training, and UnifiedPUFNet at one seed ranked replays above genuine readings. Replays disturbed few features, whereas clones shifted most features in magnitude. A dual-signal UnifiedPUFNet that adds a raw-magnitude input kept a mean held-out AUC above 0.99 on every family, although the clone test vectors differ from what a deployed verifier computes. Calibration and attack attribution failed on most held-out folds. These findings indicate that a raw-magnitude-aware detector is a practical candidate for PUF liveness checking, if validated on held-out attack families and thresholded at genuine-score quantiles.