A 28nm 48KOPS 3.4µJ/Op Agile Crypto-Processor for Post-Quantum Cryptography on Multi-Mathematical Problems

The advancements in quantum computing and the potential for polynomial-time solutions to traditional public key cryptography (i.e., Rivest–Shamir–Adleman (RSA) and elliptic-curve cryptography (ECC)) using Shor’s algorithm pose a serious threat to the security of pre-quantum blockchain technologies. This paper proposes an efficient quantum-safe blockchain that incorporates new quantum-safe consensus algorithms. We integrate post-quantum signature schemes into the blockchain’s transaction signing and verification processes to enhance resistance against quantum attacks. Specifically, we employ the Falcon signature scheme, which was selected during the NIST post-quantum cryptography (PQC) standardization process. Although the integration of the post-quantum signature scheme results in a reduction in the blockchain’s transactions per second (TPSs), we introduce efficient approaches to mitigate this performance degradation. Our proposed post-quantum delegated proof of luck (PQ-DPoL) combines a proof of luck (PoL) mechanism with a delegated approach, ensuring quantum resistance, energy efficiency, and fairness in block generation. Experimental results demonstrate that while post-quantum cryptographic algorithms like Falcon introduce larger signature sizes and slower processing times, the PQ-DPoL algorithm effectively balances security and performance, providing a viable solution for secure blockchain operations in a post-quantum era.

This thesis covers the mathematics of isogeny-based cryptography and presents the SQISign digital signature.SQISign is a digital signature, submitted to NIST's post-quantum cryptography standardization program.The thesis also covers the security notions for digital signatures called Beyond UnForgeability Features (BUFF) and discusses these security notions within the context of SQISign.A general transformation for digital signatures, called the BUFF transform, can be used to achieve all BUFF notions.This thesis ends with an application of the BUFF transform to SQISign and analyzes the performance impact.

NIST is in the process of evaluating public-key digital signature algorithms for potential standardization to protect sensitive information into the foreseeable future, including after the advent of quantum computers. Any signature scheme that is eventually selected would augment FIPS 204, Module-Lattice-Based Digital Signature Standard; FIPS 205, Stateless Hash-Based Digital Signature Standard; FIPS 186-5, Digital Signature Standard (DSS); and SP 800-208, Recommendation for Stateful Hash-Based Signature Schemes. This report describes the evaluation criteria and selection process of the First Round of the Additional Digital Signatures for the NIST Post-Quantum Cryptography (PQC) Standardization Process. Based on public feedback and internal reviews of the first-round candidates, NIST selected14 candidate algorithms to move forward to the second round of evaluation: CROSS, FAEST, HAWK, LESS, MAYO, Mirath (merger of MIRA/MiRitH), MQOM, PERK, QR-UOV, RYDE, SDitH, SNOVA, SQIsign, and UOV.

Post-Quantum Cryptography (PQC) has emerged as a response of the cryptographic community to the danger of attacks performed using quantum computers. All PQC schemes can be implemented in software and hardware using conventional (non-quantum) computing systems. PQC is the biggest revolution in cryptography since the invention of public-key schemes in the mid-1970 s. Lattice-based key exchange schemes have emerged as leading candidates in the NIST PQC standardization process due to their relatively short public keys and ciphertexts. This paper presents novel high-speed hardware architectures for four lattice-based Key Encapsulation Mechanisms (KEMs) representing three NIST PQC finalists: NTRU (with two distinct variants, NTRU-HPS and NTRU-HRSS), CRYSTALS-Kyber, and Saber. We benchmark these candidates in terms of their performance and resource utilization in today's FPGAs. Our best architectures outperform the best designs from other groups reported to date in terms of the area-time product by factors ranging from 1.01 to 2.88, depending on the algorithm and security level. Additionally, our study demonstrates that CRYSTALS-Kyber and Saber have very similar hardware performance. Both outperform NTRU in terms of execution time by a factor 36-62 for key generation and 3-7 for decapsulation, assuming the same security level.

The emergence of large-scale quantum computing presents an imminent threat to contemporary public-key cryptosystems, with quantum algorithms such as Shor’s algorithm capable of efficiently breaking RSA and elliptic curve cryptography (ECC). This vulnerability has catalyzed accelerated standardization efforts for post-quantum cryptography (PQC) by the U.S. National Institute of Standards and Technology (NIST) and global security stakeholders. While theoretical security analysis of these quantum-resistant algorithms has advanced considerably, comprehensive real-world performance benchmarks spanning diverse computing environments—from high-performance cloud infrastructure to severely resource-constrained IoT devices—remain insufficient for informed deployment planning. This paper presents the most extensive cross-platform empirical evaluation to date of NIST-selected PQC algorithms, including CRYSTALS-Kyber and NTRU for key encapsulation mechanisms (KEMs), alongside BIKE as a code-based alternative, and CRYSTALS-Dilithium and Falcon for digital signatures. Our systematic benchmarking framework measures computational latency, memory utilization, key sizes, and protocol overhead across multiple security levels (NIST Levels 1, 3, and 5) in three distinct hardware environments and various network conditions. Results demonstrate that contemporary server architectures can implement these algorithms with negligible performance impact (<5% additional latency), making immediate adoption feasible for cloud services. In contrast, resource-constrained devices experience more significant overhead, with computational demands varying by up to 12× between algorithms at equivalent security levels, highlighting the importance of algorithm selection for edge deployments. Beyond standalone algorithm performance, we analyze integration challenges within existing security protocols, revealing that naive implementation of PQC in TLS 1.3 can increase handshake size by up to 7× compared to classical approaches. To address this, we propose and evaluate three optimization strategies that reduce bandwidth requirements by 40–60% without compromising security guarantees. Our investigation further encompasses memory-constrained implementation techniques, side-channel resistance measures, and hybrid classical-quantum approaches for transitional deployments. Based on these comprehensive findings, we present a risk-based migration framework and algorithm selection guidelines tailored to specific use cases, including financial transactions, secure firmware updates, vehicle-to-infrastructure communications, and IoT fleet management. This practical roadmap enables organizations to strategically prioritize systems for quantum-resistant upgrades based on data sensitivity, resource constraints, and technical feasibility. Our results conclusively demonstrate that PQC is deployment-ready for most applications, provided that implementations are carefully optimized for the specific performance characteristics and security requirements of target environments. We also identify several remaining research challenges for the community, including further optimization for ultra-constrained devices, standardization of hybrid schemes, and hardware acceleration opportunities.

This paper presents a side-channel analysis (SCA) on key encapsulation mechanism (KEM) based on the Fujisaki–Okamoto (FO) transformation and its variants. The FO transformation has been widely used in actively securing KEMs from passively secure public key encryption (PKE), as it is employed in most of NIST post-quantum cryptography (PQC) candidates for KEM. The proposed attack exploits side-channel leakage during execution of a pseudorandom function (PRF) or pseudorandom number generator (PRG) in the re-encryption of KEM decapsulation as a plaintext-checking oracle that tells whether the PKE decryption result is equivalent to the reference plaintext. The generality and practicality of the plaintext-checking oracle allow the proposed attack to attain a full-key recovery of various KEMs when an active attack on the underlying PKE is known. This paper demonstrates that the proposed attack can be applied to most NIST PQC third-round KEM candidates, namely, Kyber, Saber, FrodoKEM, NTRU, NTRU Prime, HQC, BIKE, and SIKE (for BIKE, the proposed attack achieves a partial key recovery). The applicability to Classic McEliece is unclear because there is no known active attack on this cryptosystem. This paper also presents a side-channel distinguisher design based on deep learning (DL) for mounting the proposed attack on practical implementation without the use of a profiling device. The feasibility of the proposed attack is evaluated through experimental attacks on various PRF implementations (a SHAKE software, an AES software, an AES hardware, a bit-sliced masked AES software, and a masked AES hardware based on threshold implementation). Although it is difficult to implement the oracle using the leakage from the TI-based masked hardware, the success of the proposed attack against these implementations (even except for the masked hardware), which include masked software, confirms its practicality.

The rapid advancement in quantum technology has initiated a new round of post-quantum cryptography (PQC) related exploration. The key encapsulation mechanism (KEM) Saber is an important module lattice-based PQC, which has been selected as one of the PQC finalists in the ongoing National Institute of Standards and Technology (NIST) standardization process. On the other hand, however, efficient hardware implementation of KEM Saber has not been well covered in the literature. In this paper, therefore, we propose a novel cyclic-row oriented processing (CROP) strategy for efficient implementation of the key arithmetic operation of KEM Saber, i.e., the polynomial multiplication. The proposed work consists of three layers of interdependent efforts: (i) first of all, we have formulated the main operation of KEM Saber into desired mathematical forms to be further developed into CROP based algorithms, i.e., the basic version and the advanced higher-speed version; (ii) then, we have followed the proposed CROP strategy to innovatively transfer the derived two algorithms into desired polynomial multiplication structures with the help of a series of algorithm-architecture co-implementation techniques; (iii) finally, detailed complexity analysis and implementation results have shown that the proposed polynomial multiplication structures have better area-time complexities than the state-of-the-art solutions. Specifically, the field-programmable gate array (FPGA) implementation results show that the proposed design, e.g., the basic version has at least less 11.2% area-delay product (ADP) than the best competing one (Cyclone V device). The proposed high-performance polynomial multipliers offer not only efficient operation for output results delivery but also possess low-complexity feature brought by CROP strategy. The outcome of this work is expected to provide useful references for further development and standardization process of KEM Saber.

This research paper evaluates the feasibility of cold boot attacks on the Supersingular Isogeny Key Encapsulation (SIKE) mechanism. This key encapsulation mechanism has been included in the list of alternate candidates of the third round of the National Institute of Standards and Technology (NIST) Post-Quantum Cryptography Standardization Process. To the best of our knowledge, this is the first time this scheme is assessed in the cold boot attacks setting. In particular, our evaluation is focused on the reference implementation of this scheme. Furthermore, we present a dedicated key-recovery algorithm for SIKE in this setting and show that the key recovery algorithm works for all the parameter sets recommended for this scheme. Moreover, we compute the success rates of our key recovery algorithm through simulations and show the key recovery algorithm may reconstruct the SIKE secret key for any SIKE parameters for a fixed and small α=0.001 (the probability of a 0 to 1 bit-flipping) and varying values for β (the probability of a 1 to 0 bit-flipping) in the set {0.001,0.01,…,0.1}. Additionally, we show how to integrate a quantum key enumeration algorithm with our key-recovery algorithm to improve its overall performance.

This work presents the first hybrid crypto system-on-chip (SoC) for secure neural interfacing based on post-quantum cryptography (PQC) and lightweight cryptography (LWC). The transmitted signals are protected through a two-stage hybrid cryptosystem, in which handshake and data encryption are protected by PQC and LWC, respectively. Algorithms are well explored for PQC and LWC, including Falcon digital signature algorithm (DSA), NTRU key encapsulation mechanism (KEM), and ASCON authenticated encryption (AE). Hardware cost is minimized through algorithm-architecture optimizations, including serial-based number theoretic transform (NTT), integrated polynomial multiplication, MK modular multiplication, and shuffling-based fixed-type polynomial sampling. Fabricated in 40-nm CMOS, the SoC integrates 921 K logic gates in core area of 0.62 mm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>. The chip supports post-quantum secure neural interfacing for various data formats. The power consumption is 4.8 mW at 50 MHz from a 0.7 V supply. This chip consumes <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.7~\mu $</tex-math> </inline-formula>J/OP and 48 pJ/B for the PQC handshake and LWC data encryption, respectively. Compared to the state-of-the-art designs, the SoC dissipates 12-to-<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$46\times $</tex-math> </inline-formula> less energy and achieves 1.3-to-<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$168\times $</tex-math> </inline-formula> higher area efficiency for the handshake, and dissipates <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7\times $</tex-math> </inline-formula> less energy and achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3\times $</tex-math> </inline-formula> higher area efficiency for data encryption.

Blockchain technology ensures accountability, transparency, and redundancy, but its reliance on public‐key cryptography makes it vulnerable to quantum computing threats. This article addresses the urgent need for quantum‐safe blockchain solutions by integrating post‐quantum cryptography (PQC) into blockchain frameworks. Utilizing algorithms from the NIST PQC standardization process, it is aimed to fortify blockchain security and resilience, particularly for IoT and embedded systems. Despite the importance of PQC, its implementation in blockchain systems tailored for embedded environments remains underexplored. A quantum‐secure blockchain architecture is proposed, evaluating various PQC primitives and optimizing transaction sizes through techniques such as public‐key recovery for Falcon, achieving up to 17% reduction in transaction size. The analysis identifies Falcon‐512 as the most suitable algorithm for quantum‐secure blockchains in computer‐based environments and XMSS as a viable but unsatisfactory stateful alternative. However, for embedded‐based blockchains, Dilithium demonstrates a higher transactions‐per‐second (TPS) rate compared to Falcon, primarily due to Falcon's slower signing performance on ARM CPUs. This highlights the signing time as a critical limiting factor within embedded blockchains. Additionally, smart contract functionality is integrated, assessing the impact of PQC on smart contract authentication. The findings demonstrate the feasibility and practicality, paving the way for robust and future‐proof IoT applications.

The appearance of quantum computing in the short foreseeable future and its capability to break conventional cryptographic algorithms forces to change the paradigm of secure real‐time communications. Thus, government organizations, data centers, and enterprises among others are migrating their public key infrastructure towards using post‐quantum cryptography (PQC) algorithms in order to mitigate the security threats posed by quantum computers. This letter presents the first quantum resilient secure end‐to‐end communication link based on PQC algorithms operating between two data‐processing units DPU. Both data‐processing units employ on‐board ARM processors to perform the computationally expensive cryptographic building blocks—in that case CRYSTALS‐Kyber as a key encapsulation mechanism and CRYSTALS‐Dilithium for digital signature scheme in combination with advanced encryption standard with 256‐bit key.

Classic McEliece has gathered attention as a candidate in NIST post-quantum cryptography standardization. However, it suffers from high demands on the decryption algorithm, making it unsuitable for resource-constrained devices. In this paper, we propose a novel implementation method, named giant footprint sharing, that reduces memory size during decryption in Classic McEliece. The decryption algorithm processes a large number of intermediate variables computed from a secret key in memory. The giant footprint sharing identifies the largest variable among them and allocates a memory-sharing structure to store it, thereby reducing the overall memory size regardless of the implementation platform. The giant footprint sharing can also be combined with existing acceleration techniques, such as fast Fourier transformation. We evaluate Classic McEliece with the giant footprint sharing on the Arm Cortex-M33 CPU and show that it reduces memory size by up to 46% without significant degradation in computation time compared with the existing fast-implementation by Chen et al. (at TCHES 2021). Extensive experiments with the giant footprint sharing further reveal that it maintains a constant memory size regardless of the compiler optimization, and it also achieves an optimal balance in the trade-off between memory size and computation time. The giant footprint sharing is remarkable for any scheme, that contains a large-scale matrix computation and the life cycle for each variable is limited.

The rapid expansion of the Internet of Things (IoT) has introduced unprecedented cybersecurity challenges, as traditional cryptographic methods struggle to provide both efficiency and quantum resistance in resource-constrained environments. This research introduces Adaptive Quantum Cryptography (AQC), a novel security framework that integrates Quantum Key Distribution (QKD), Post-Quantum Cryptography (PQC), Quantum Random Number Generators (QRNGs), and AI-driven dynamic adaptation to safeguard IoT networks against evolving cyber threats. Unlike classical encryption techniques such as RSA and ECC, which are vulnerable to quantum attacks, AQC leverages quantum mechanical principles—such as superposition, entanglement, and the no-cloning theorem—to ensure provably secure key exchange and data encryption. The proposed AQC system dynamically adjusts cryptographic protocols based on real-time network conditions, computational resources, and threat intelligence, optimizing security without imposing excessive overhead. Experimental evaluations demonstrate that AQC achieves a 70% improvement in key exchange efficiency, a 50% reduction in computational load, and a 95% resilience rate against quantum attacks compared to conventional encryption methods. Additionally, the integration of QRNGs enhances cryptographic key entropy, while AI-driven anomaly detection enables proactive threat mitigation. This framework is particularly suited for next-generation IoT applications, including smart grids, autonomous vehicles, and industrial automation, where security, scalability, and energy efficiency are critical. By combining quantum-resistant algorithms with adaptive security mechanisms, AQC provides forward secrecy, resistance to quantum adversaries, and robust authentication, ensuring long-term protection for IoT ecosystems in the post-quantum era. The findings underscore AQC’s potential as a foundational security architecture for future quantum-safe IoT networks.

As artificial intelligence (AI) systems increasingly underpin critical applications in healthcare, finance, defense, and infrastructure, ensuring their security has become paramount. However, the rapid advancement of quantum computing poses a significant threat to classical cryptographic schemes that currently safeguard these systems. Quantum algorithms such as Shor’s and Grover’s threaten to undermine the confidentiality, integrity, and authentication of data and models that AI systems depend on. In this context, Post-Quantum Cryptography (PQC) has emerged as a crucial area of research aimed at developing cryptographic algorithms that are secure against both classical and quantum adversaries. This paper investigates the intersection of quantum computing threats and AI system vulnerabilities, highlighting how current security mechanisms may fail in a post-quantum era. We explore various categories of PQC—including lattice-based, hash-based, and multivariate polynomial cryptography—and evaluate their applicability to securing AI systems, particularly in scenarios such as model distribution, federated learning, data encryption, and communication in AI-enabled edge and IoT environments. Through analysis of emerging use cases and proposed architectures, the paper outlines how PQC can be integrated into the AI lifecycle to mitigate quantum-era threats. It also identifies key challenges such as computational overhead, integration complexity, and standardization gaps. The study concludes by outlining future research opportunities, including the development of efficient PQC protocols tailored to AI workloads, the creation of quantum-resilient AI pipelines, and the importance of interdisciplinary collaboration to secure AI’s future.

As artificial intelligence (AI) systems increasingly underpin critical applications in healthcare, finance, defense, and infrastructure, ensuring their security has become paramount. However, the rapid advancement of quantum computing poses a significant threat to classical cryptographic schemes that currently safeguard these systems. Quantum algorithms such as Shor’s and Grover’s threaten to undermine the confidentiality, integrity, and authentication of data and models that AI systems depend on. In this context, Post-Quantum Cryptography (PQC) has emerged as a crucial area of research aimed at developing cryptographic algorithms that are secure against both classical and quantum adversaries. This paper investigates the intersection of quantum computing threats and AI system vulnerabilities, highlighting how current security mechanisms may fail in a post-quantum era. We explore various categories of PQC—including lattice-based, hash-based, and multivariate polynomial cryptography—and evaluate their applicability to securing AI systems, particularly in scenarios such as model distribution, federated learning, data encryption, and communication in AI-enabled edge and IoT environments. Through analysis of emerging use cases and proposed architectures, the paper outlines how PQC can be integrated into the AI lifecycle to mitigate quantum-era threats. It also identifies key challenges such as computational overhead, integration complexity, and standardization gaps. The study concludes by outlining future research opportunities, including the development of efficient PQC protocols tailored to AI workloads, the creation of quantum-resilient AI pipelines, and the importance of interdisciplinary collaboration to secure AI’s future.