Module-Lattice-Based Key-Encapsulation Mechanism Performance Measurements

Rapid advances in quantum computing, together with the announcement by the National Institute of Standards and Technology (NIST) to define new standards for digitalsignature, encryption, and key-establishment protocols, have created significant interest in post-quantum cryptographic schemes. This paper introduces Kyber (part of CRYSTALS - Cryptographic Suite for Algebraic Lattices - a package submitted to NIST post-quantum standardization effort in November 2017), a portfolio of post-quantum cryptographic primitives built around a key-encapsulation mechanism (KEM), based on hardness assumptions over module lattices. Our KEM is most naturally seen as a successor to the NEWHOPE KEM (Usenix 2016). In particular, the key and ciphertext sizes of our new construction are about half the size, the KEM offers CCA instead of only passive security, the security is based on a more general (and flexible) lattice problem, and our optimized implementation results in essentially the same running time as the aforementioned scheme. We first introduce a CPA-secure public-key encryption scheme, apply a variant of the Fujisaki-Okamoto transform to create a CCA-secure KEM, and eventually construct, in a black-box manner, CCA-secure encryption, key exchange, and authenticated-key-exchange schemes. The security of our primitives is based on the hardness of Module-LWE in the classical and quantum random oracle models, and our concrete parameters conservatively target more than 128 bits of postquantum security.

Facing the potential threat raised by quantum computing, a great deal of research from many groups and industrial giants has gone into building public‐key post‐quantum cryptographic primitives that are resistant to the quantum attackers. Among them, there is a large number of post‐quantum key encapsulation mechanisms (KEMs), whose purpose is to provide a secure key exchange, which is a very crucial component in public‐key cryptography. This paper presents a formal security analysis of three lattice‐based KEMs including Kyber, Saber, and SK‐MLWR. We use Maude, a specification language supporting equational and rewriting logic and a high‐performance tool equipped with many advanced features, such as a reachability analyzer that can be used as a model checker for invariant properties, to model the three KEMs as state machines. Because they all belong to the class of lattice‐based KEMs, they share many common parts in their designs, such as polynomials, vectors, and message exchange patterns. We first model these common parts and combine them into a specification, called base specification. After that, for each of the three KEMs, by extending the base specification, we just need to model some additional parts and the mechanism execution. Once completing the three specifications, we conduct invariant model checkings with the Maude search command, pointing out a similar man‐in‐the‐middle attack. The occurrence of this attack is due to the fact that authentication is not part of the KEMs, and therefore an active attacker can modify all communication between two honest parties.

Abstract: This chapter delves into the transformative synergies between quantum computing, artificial intelligence (AI), and blockchain technology, focusing on their revolutionary impact on security, decentralized systems, and biomedical science. Quantum computing’s unparalleled computational power, combined with AI’s predictive analytics, enhances data security through quantum-resistant cryptography and advanced threat detection systems. In blockchain, the integration of quantum and AI optimizes scalability, improves transaction efficiency, and fortifies decentralized networks against quantum attacks. In biomedical science, these technologies accelerate drug discovery, enable precise genomic analysis, and enhance personalized medicine through AI-driven insights and quantum simulations. The chapter also addresses key challenges such as data privacy, ethical considerations, and scalability issues, while showcasing real-world applications and success stories in industries like healthcare, finance, and IoT. It concludes with a forward-looking perspective on fostering interdisciplinary collaboration and innovation to harness these quantum synergies for global societal benefit. Keywords Quantum computing, artificial intelligence, blockchain, quantum cryptography, decentralized systems, data security, AI-driven analytics, biomedical science, drug discovery, personalized medicine, quantum-resistant blockchain, genomic analysis, quantum simulations, interdisciplinary collaboration, innovation.

The article is devoted to the impact of artificial intelligence (AI) on financial management system, efficiency and feasibility of introducing mechanisms, technologies and programs based on artificial intelligence into the work of a financial structure. It describes the essence of artificial intelligence and its features. The article uses the following research methods: analysis, synthesis, observation, measurement, comparison, financial analysis, as well as data processing and interpretation. The relevance and scientific novelty of research consists in the analysis and determination of the relationship between the key performance indicators of financial management and the use of mechanisms, technologies and programs based on artificial intelligence; identification of the degree of influence of technologies based on artificial intelligence on the effectiveness of the financial management system of commercial organizations. A scientific research on the influence of AI on the financial management system in a commercial organization is conducted using the example of PJSC Magnit, the largest retail chain of stores founded in Krasnodar. The data for the study were taken from the official website of the organization and from the financial and non-financial reports and documents provided therein. The degree of digitalization of the company, the systems used based on artificial intelligence are studied, and the work of the company’s financial management system is analyzed. The efficiency of the financial system is studied, the indicators related to the company’s financial management system are analyzed. The relationship between investments in the creation of IT departments, AI systems and their use in the financial system with the company’s financial indicators is revealed. A conclusion is made about the positive impact of the implementation of mechanisms using artificial intelligence in the work of the financial management system to achieve its goals and maintain competitiveness. The measures are suggested for commercial organizations on the methods and forms of the use of artificial intelligence system.

In today’s advanced network and digital age, the Internet of Things network is experiencing a significant growing trend and, due to its wide range of services and network coverage, has been able to take a special place in today’s technology era. Among the applications that can be mentioned for this network are the field of electronic health, smart residential complexes, and a wide level of connections that have connected the inner-city infrastructure in a complex way to make it smart. The notable and critical issue that exists in this network is the extent of the elements that make up the network and, due to this, the strong and massive data exchanges at the network level. With the increasing deployment of the Internet of Things, a wide range of challenges arise, especially in the discussion of establishing network security. Regarding security concerns, ensuring the confidentiality of the data being exchanged in the network, maintaining the privacy of the network nodes, protecting the identity of the network nodes, and finally implementing the security policies required to deal with a wide range of network cyber threats are of great importance. A fundamental element in the security of IoT networks is the authentication process, wherein nodes are required to validate each other’s identities to ensure the establishment of secure communication channels. Through the enforcement of security prerequisites, in this study, we suggested a security protocol focused on reinforcing security characteristics and safeguarding IoT nodes. By utilizing the security features provided by Elliptic Curve Cryptography (ECC) and employing the Elliptic Curve Diffie–Hellman (ECDH) key-exchange mechanism, we designed a protocol for authenticating nodes and establishing encryption keys for every communication session within the Internet of Things. To substantiate the effectiveness and resilience of our proposed protocol in withstanding attacks and network vulnerabilities, we conducted evaluations utilizing both formal and informal means. Furthermore, our results demonstrate that the protocol is characterized by low computational and communication demands, which makes it especially well-suited for IoT nodes operating under resource constraints.

Meet-in-the-middle (MITM) is a general paradigm where internal states are computed along two independent paths (‘forwards’ and ‘backwards’) that are then matched. Over time, MITM attacks improved using more refined techniques and exploiting additional freedoms and structure, which makes it more involved to find and optimize such attacks. This has led to the use of detailed attack models for generic solvers to automatically search for improved attacks, notably a MILP model developed by Bao et al. at EUROCRYPT 2021. In this paper, we study a simpler MILP modeling combining a greatly reduced attack representation as input to the generic solver, together with a theoretical analysis that, for any solution, proves the existence and complexity of a detailed attack. This modeling allows to find both classical and quantum attacks on a broad class of cryptographic permutations. First, Present-like constructions, with the permutations from the Spongent hash functions: we improve the MITM step in distinguishers by up to 3 rounds. Second, AES-like designs: despite being much simpler than Bao et al.’s, our model allows to recover the best previous results. The only limitation is that we do not use degrees of freedom from the key schedule. Third, we show that the model can be extended to target more permutations, like Feistel networks. In this context we give new Guess-and-determine attacks on reduced Simpira v2 and Sparkle. Finally, using our model, we find several new quantum preimage and pseudo-preimage attacks (e.g. Haraka v2, Simpira v2...) targeting the same number of rounds as the classical attacks.

Diffie-Hellman (D-H) key exchange protocol, first published in 1976 is one of the most distinguished and widely used secured key exchange protocols. The protocol makes it possible to exchange a symmetric key between two parties over an unsecured communication channel without pre-agreement of any shared secret. The D-H key exchange protocol however requires a pre-set public parameters P (a large prime) and g a generator of ℤp to be public i.e. known to all users of the system. This paper proposes a key exchange mechanism without any public parameter. Unlike D-H key exchange mechanism our scheme uses encryption to transmit intermediate data during key exchange stage, thus it is non-vulnerable to man-in-the-middle attach.

Authenticated key exchange protocol is widely applied in the Internet services. Lattice-based key exchange protocols turn out to be quantum-resistant and hence have attracted tremendous attention. A lattice-based explicit authenticated key exchange protocol is constructed by combining an IND-CPA key encapsulation mechanism with a strong EUF-CMA digital signature under the message-recovery mode. Concrete parameter specifications are suggested under the 102 and 218 bits post-quantum security, respectively. Compared with the implicit authenticated key exchange derived directly from the key encapsulation mechanism, our proposals reduce the communication costs by 21.7% and 25.7%, respectively, under the same post-quantum security level. Rough analysis shows that the proposals also obtain some advantages over previous constructions in term of computational efficiency. Moreover, our scheme achieves perfect forward secrecy while the original scheme only satisfies weak forward secrecy.

Advances in quantum computers may pose a significant threat to existing public-key encryption methods, which are crucial to the current infrastructure of cyber security. Both RSA and ECDSA, the two most widely used security algorithms today, may be (in principle) solved by the Shor algorithm in polynomial time due to its ability to efficiently solve the discrete logarithm problem, potentially making present infrastructures insecure against a quantum attack. The National Institute of Standards and Technology (NIST) reacted with the post-quantum cryptography (PQC) standardization process to develop and optimize a series of post-quantum algorithms (PQAs) based on difficult mathematical problems that are not susceptible to being solved by Shor’s algorithm. Whilst high-powered computers can run these PQAs efficiently, further work is needed to investigate and benchmark the performance of these algorithms on lower-powered (constrained) devices and the ease with which they may be integrated into existing protocols such as TLS. This paper provides quantitative benchmark and handshake performance data for the most recently selected PQAs from NIST, tested on a Raspberry Pi 4 device to simulate today’s IoT (Internet of Things) devices, and provides quantitative comparisons with previous benchmarking data on a range of constrained systems. CRYSTALS-Kyber and CRYSTALS-Dilithium are shown to be the most efficient PQAs in the key encapsulation and signature algorithms, respectively, with Falcon providing the optimal TLS handshake size.

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Group key exchange</i> is a promising cryptographic protocol that has been studied extensively over the years. However, an overwhelming majority of them involve interaction among users. This makes them unsuitable in a cloud-based outsourced storage systems where users seek to store and share their files with their collaborators who stay offline until they want to access the file. To address this concern, Boyd et al. designed an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">offline assisted group key exchange</i> (OAGKE) protocol that offers forward secrecy. The core component of their OAGKE protocol is a variant of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">key encapsulation mechanism</i> (KEM) called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">blinded</i> KEM (BKEM). However, their constructions of BKEM are susceptible to quantum attacks. This work proposes an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">isogeny-based</i> BKEM and a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">forward secure</i> OAGKE that is secure against adversaries equipped with quantum computers. Our construction of BKEM is based on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Commutative Supersingular Isogeny Diffie-Hellman</i> (CSIDH) cryptosystem. We provide a formal security proof demonstrating the security of our scheme. Furthermore, we manifest an instantiation of our BKEM from CSIDH-512 parameter set. Finally, we present our isogeny-based OAGKE protocol along with its rigorous security analysis.

The advancements in quantum computing and quantum cryptology have recently started to gain momentum and transformation of usable quantum technologies from dream to reality has begun to look viable. This has created an immediate requirement to comprehend quantum attacks and their cryptographic implications, which is a prerequisite obligation to design cryptographic systems resistant to current and futuristic projected quantum and conventional attacks. In this context, this paper reviews the prevalent quantum concepts and analyses their envisaged impact on various aspects of modern-day communication and information security technologies. Moreover, the paper also presents six open-problems and two conjectures, which are formulated to define prerequisite technological obligations for fully comprehending the futuristic quantum threats to contemporary communication security technologies and information assets processed through these systems. Furthermore, the paper also presents some important concepts in the form of questions and discusses some recent trends adapted in cryptographic designs to thwart quantum attacks.

As quantum computing development advances closer toward achieving fault-tolerant error-corrected realisation, debates on applications, impacts, risks, and benefits of quantum computing are timely and due. While there is awareness of the potential power and complexity of quantum computers, there has been relatively little attention on the social impacts and ethical implications of this technology. In grappling with the social impacts of quantum computing, some stakeholders have turned to applied ethics, specifically (classical) computer and Artificial Intelligence (AI) ethics, for guidance. While computer and AI ethics are useful starting points for evaluating the ethical issues posed by quantum computing, uncritically applying existing sets of ethical principles risks overlooking how quantum computing differs from these technologies. We argue that borrowing of ethical principles and guidelines from AI and computing is inappropriate for several reasons: (1) quantum computing, classical computing, and AI are different technologies with significant material differences; (2) unlike AI and classical computers which have become established, quantum computing is an emerging technology, which has implications on the levels of accessibility and immediate impact in society; and (3) there are significant differences in the way AI and quantum computing have been developed and by whom. We also briefly summarise some of the unique risks and societ al impacts posed by quantum computing. We posit that these reasons support the argument for a new sub-field of quantum computing ethics, which would allow a relevant scholarship to develop and provide guidance as quantum computing technology continues to mature.

The rapid development of quantum computing constitutes a significant threat to modern Public-Key Cryptography (PKC). key, signature and Key Encapsulation Mechanism (KEM) sysThe use of Shor's algorithm [1] with potential powerful tem based on a double exponentiation with matrix exponents. quantum computers could easily break the two most widely In this paper, a high-throughput pipelined architecture of used public key cryptosystems, namely, RSA and Elliptic DME-(3,2,48), with $m =3 , n =2$, and $q = 2 ^{e}\,= 2 ^{48}$, has been Curve Cryptography (ECC), based on integer factorization and presented and hardware implementations over Xilinx FPGAs discrete logarithm problems. For this reason, Post-Quantum Artix-7 have been performed. These settings correspond with Cryptography (PQC) [2] based on alternative mathematical the reference implementation given in the NIST proposal [7], features has become a fundamental research topic due to its where the finite field $GF( 2 ^{48})$ is generated by the type I resistance against quantum computers. The National Institute of Standards and Technology (NIST) has even opened a call for proposals of quantum-resistant PKC algorithms in order to standardize one or more PQC algorithms. Cryptographic systems that appear to be extremely difficult to break with large quantum computers are hash-based cryptography, lattice-based cryptography, code-based cryptography, and multivariate-quadratic cryptography [3]. Furthermore, efficient hardware implementations are highly required for these alternative quantum-resistant cryptosystems [4].

Quantum computing (QC) is not a futuristic notion in agriculture, though its full potential has yet to be realized. QC is an emerging field at the intersection of physics and computer science that holds immense potential to revolutionize various sectors, including agriculture production and artificial intelligence (AI) modeling. While QC is still in the early stages of development and practical applications within agriculture are not yet widespread, researchers are actively exploring its potential benefits in various agricultural domains, including crop optimization, livestock breeding, and environmental monitoring. QC harnesses the principles of quantum mechanics to perform computations using quantum bits or qubits, which can exist in multiple states simultaneously. Unlike classical computers, which rely on binary bits representing 0 or 1, quantum computers exploit phenomena such as superposition and entanglement to process information in parallel, potentially offering exponential speedup for certain types of problems. In agriculture production, particularly in animal science, QC offers promising avenues for optimizing processes and enhancing productivity. Quantum algorithms can analyze vast amounts of genomic data to improve breeding programs, leading to the development of more resilient and productive livestock breeds. Furthermore, QC can facilitate precision farming techniques by modeling complex environmental factors and animal behavior to optimize feeding strategies, disease management, and overall farm management practices. Moreover, QC can significantly benefit AI modeling by accelerating computations and enabling more efficient training of AI models. Quantum algorithms can enhance the performance of AI algorithms in various tasks, including pattern recognition, natural language processing, and predictive analytics. By leveraging quantum-enhanced optimization algorithms, AI models can achieve better convergence and accuracy, leading to more effective decision-making and problem-solving capabilities. While hybrid intelligent models also represent a novel frontier in agriculture, QC has the potential to expedite the merging of mechanistic and AI modeling paradigms, facilitating a more holistic understanding of complex systems in agriculture and beyond. By integrating mechanistic models, which describe the underlying physical processes, with AI models, which learn patterns from data, quantum computing can enable comprehensive simulations and predictions of agricultural systems. This fusion of modeling paradigms can lead to more accurate and robust predictions of crop yields, livestock performance, and environmental impacts, facilitating informed decision-making for farmers and policymakers. The application of QC in agriculture, however, requires interdisciplinary collaborations between physicists, computer scientists, agronomists/animal scientists, and AI researchers. These collaborations can drive the development of quantum algorithms tailored to agricultural applications, the integration of quantum-enhanced AI techniques into existing modeling frameworks, and the deployment of QC resources in real-world agricultural systems. Ultimately, harnessing the power of QC holds the potential to revolutionize agriculture production practices, including regenerative agriculture, and advance AI modeling capabilities, paving the way for a more sustainable and efficient agricultural industry.

Distributed Ledger Technologies (DLTs), including but not limited to Blockchain, have become key enablers of digital transformation across sectors. Their core attributes — immutability, traceability, decentralization, and tamper resistance — make them well-suited for secure and transparent data management. While initially linked to cryptocurrencies, DLTs are now applied in diverse domains such as identity verification, supply chain management, secure voting, and digital asset certification.In parallel, rapid advances in artificial intelligence (AI), particularly in Generative AI, are reshaping how data is generated, interpreted and applied. AI enables new forms of automation and decision-making, but it also introduces risks related to misinformation, digital fraud and security vulnerabilities. The convergence of DLT and AI offers a promising framework to address these challenges. As decentralized trust infrastructures, DLTs can enhance the transparency and integrity of AI systems by validating identities, certifying data provenance and securing sensitive assets such as contracts, medical records and intellectual property.This integration fits within the broader BIBI paradigm – Blockchain, AI, Big Data and the Internet of Things (IoT), where DLTs provide the foundation for secure, distributed coordination and auditability. Coupled with AI, they enable automated decision-making over large-scale, heterogeneous data, particularly from IoT devices, with applications in manufacturing, logistics and health care. This synergy also unlocks advanced functionalities, including decentralized identity, verifiable credentials and trusted automation, facilitating use cases such as tokenized assets and secure digital certification.Nevertheless, several technical and ethical challenges remain. The increasing computational demands of AI raise concerns about energy consumption, an issue already encountered in early DLT deployments. Energy-efficient consensus mechanisms, such as Proof-of-Stake and Byzantine Fault Tolerance, offer valuable insights for developing more sustainable AI infrastructures. Another key challenge is interoperability, as most DLT platforms operate in isolation. Enhancing interoperability would enable seamless data and asset exchange across heterogeneous ledgers, contributing to a more integrated and user-centric digital ecosystem.Looking forward, the convergence of DLT and AI holds significant potential for intelligent smart contracts, advanced traceability in critical sectors such as food safety and health care, and the development of digital twins for real-time simulation and optimization of organizational processes. This cross-disciplinary innovation can drive not only technological and economic growth but also democratic access to trusted, privacy-preserving, and sustainable digital systems.The Special Issue “Distributed Ledger Technologies and Artificial Intelligence” aims to offer a dedicated forum for researchers exploring the intersection of these two rapidly evolving fields. A total of 13 submissions were received, and following a rigorous double-blind peer review process, with at least three reviews per manuscript, three articles were selected for publication.The first article, “Energy: Reducing Latency in IoT DLTs for AI-Driven Real-Time Solutions” (Moya Perez et al., 2025), addresses the challenges of integrating IoT networks with DLT and AI, focusing on latency and energy efficiency. The authors propose Energy, a novel consensus algorithm designed for public DAG-based DLTs, which reduces or eliminates Proof-of-Work requirements. Experimental results demonstrate substantial improvements in low-payload scenarios common in IoT, enabling real-time AI model updates and efficient, secure data flow in large-scale environments.The second paper, “eFLEET: A Framework in Federated Learning for Enhanced Electric Transportation” (Ruiz de Gauna et al., 2025), presents a federated learning approach for optimizing autonomous electric vehicle routing in urban settings. Using traffic image analysis through computer vision and a DAG-based privacy layer, the framework selects optimal routes based on real-time and predictive data. The experiments show how the system improves traffic planning, enhances scalability and contributes to energy efficiency and pollution reduction in smart cities.The third contribution, “Enhancing the Viewing, Browsing and Searching of Knowledge Graphs with Virtual Properties” (Dibowski, 2024), introduces virtual properties as a user-interface enhancement for knowledge graph exploration. Defined using SHACL and evaluated via SPARQL queries, virtual properties enrich class scopes beyond direct neighbors. Successfully implemented at Bosch for over 100,000 users, this novel approach improves usability and expressiveness in knowledge-driven applications and sets a new direction for SHACL-based UI development.The authors would like to express their sincere gratitude to all the authors who submitted their contributions and to the anonymous reviewers for their thoughtful and rigorous evaluations. The authors also extend their appreciation to Professor Honghao Gao, Editor-in-Chief of IJWIS (Emerald), for his support in the publication of this special issue. The authors believe the selected papers represent cutting-edge research in this area and will be of great interest to the DLT and AI research communities.