Blockchain Papers

Dr. Peram Prashanthi, Ankita Biswas, Majji Prasad, Nethikunta Arun Kumar · 5 authors

Blockchain technology offers a powerful solution for securing and managing legal and academic documents by providing decentralization, transparency, and immutability. This research presents a blockchain-based framework for tamper-proof legal document and credential management aimed at improving the reliability and efficiency of traditional verification systems. In the proposed architecture, applicants submit their credentials, which are authenticated by issuing institutions and stored in the InterPlanetary File System (IPFS) for decentralized file management, while only cryptographic hashes are recorded on the blockchain to ensure integrity, reduce storage costs, and improve scalability. The system supports multiple consensus mechanisms, including Proof of Work, Proof of Stake, and Practical Byzantine Fault Tolerance, and experimental results demonstrate that Proof of Stake achieves the best balance between security and transaction efficiency. A prototype implementation achieved a transaction throughput of up to 1,000 transactions per second with an average confirmation time of 5 seconds, significantly reducing verification time and manual processing compared to conventional methods. Comparative analysis confirms that the proposed solution enhances security, minimizes fraud, and improves cost-effectiveness. The framework provides a strong foundation for future extensions such as cross-chain interoperability, AI-driven fraud detection, and mobile-based verification platforms

Likhit Mada

The convergence of blockchain technology and cloud computing has transformed traditional financial infrastructures, addressing critical scalability challenges that previously hindered widespread adoption. Cloud-native distributed ledger technology (DLT) emerges as a promising solution that leverages elastic computing resources, scalable storage solutions, and serverless architectures while preserving immutability and transparency. This integration creates hybrid architectures that balance decentralization benefits with cloud computing performance advantages, enabling financial institutions to process high-volume transactions efficiently. Major cloud providers have developed specialized blockchain-as-a-service offerings that reduce implementation barriers while ensuring regulatory compliance. These technologies revolutionize multiple financial domains: cross-border payments become faster and less expensive, trade finance processes transition from paper-based to digital workflows, and traditional assets gain unprecedented liquidity through tokenization. Despite remaining challenges in data privacy, regulatory compliance, and interoperability, cloud-native DLT represents a significant evolutionary step in financial infrastructure development, creating more inclusive, efficient systems that reshape global financial services and expand access beyond traditional banking relationships.

Ramanakar Reddy Danda

The rapid proliferation of Internet of Things (IoT) devices has ushered in a new era of connectivity and data exchange, revolutionizing various industries. However, the inherent vulnerabilities in traditional centralized transaction systems pose significant security challenges, particularly when dealing with sensitive data generated by IoT devices. This paper introduces an ICAA (Integrity Consensus Authorization Algorithm) for securing transaction records over the IoT network by leveraging Decentralized Distributed Ledger Technology (DDL), integrating the PICA (Proof-of-Integrity Consensus Algorithm) and CTAP (Context-Aware Transaction Authorization Protocol). The proposed system addresses the limitations of centralized architectures by employing a decentralized ledger, ensuring transparency, immutability, and tamper-resistant transaction records. The Proof-of-Integrity Consensus Algorithm enhances the security of the network by validating and confirming transactions based on the integrity of the data stored in the distributed ledger. This consensus mechanism minimizes the risk of fraudulent activities and unauthorized modifications, making it well-suited for the dynamic and distributed nature of IoT environments. Furthermore, the integration of the Context-Aware Transaction Authorization Protocol enhances the adaptability of the system to the diverse contexts in which IoT devices operate. The synergy between the Proof-of-Integrity Consensus Algorithm and the Context-Aware Transaction Authorization Protocol creates a comprehensive and secure framework for managing transaction records in IoT networks. . The proposed HGGC is 5.026% better than the ECMQV-MAC, 0.4215% better than QKD, and 0.0843% better than OTP in the nodes 200. The proposed model contributes to the establishment of a trustworthy and resilient infrastructure for the IoT, laying the foundation for secure and transparent transactions in the connected world.

Imane Cheikh, Sébastien Roy, Essaid Sabir, Rachid Aouami

The Massive Internet of Things (MIoT) envisions an interconnected ecosystem of billions of devices, fundamentally transforming diverse sectors such as healthcare, smart cities, transportation, agriculture, and energy management. However, the vast scale of MIoT introduces significant challenges, including network scalability, efficient data management, energy conservation, and robust security mechanisms. This paper presents a thorough review of existing and emerging MIoT technologies designed to address these challenges, including Low-Power Wide-Area Networks (LPWAN), 5G/6G capabilities, edge and fog computing architectures, and hybrid access methodologies. We further investigate advanced strategies such as AI-driven resource allocation, federated learning for privacy-preserving analytics, and decentralized security frameworks using blockchain. Additionally, we analyze sustainable practices, emphasizing energy harvesting and integrating green technologies to reduce environmental impact. Through extensive comparative analysis, this study identifies critical innovations and architectural adaptations required to support efficient, resilient, and scalable MIoT deployments. Key insights include the role of network slicing and intelligent resource management for scalability, adaptive protocols for real-time data handling, and lightweight AI models suited to the constraints of MIoT devices. This research ultimately contributes to a deeper understanding of how MIoT systems can evolve to meet the growing demand for seamless, reliable connectivity while prioritizing sustainability, security, and performance across diverse applications. Our findings serve as a roadmap for future advancements, underscoring the potential of MIoT to support a globally interconnected, intelligent infrastructure.

Farhana Javed, Josep Mangues-Bafalluy

Inter-provider agreements are central to 6G networks, where administrative domains must securely and dynamically share services. To address the dual need for transparency and confidentiality, we propose a privacy-enabled hybrid blockchain setup using Hyperledger Besu, integrating both public and private transaction workflows. The system enables decentralized service registration, selection, and SLA breach reporting through role-based smart contracts and privacy groups. We design and deploy a proof-of-concept implementation, evaluating performance using end-to-end latency as a key metric within privacy groups. Results show that public interactions maintain stable latency, while private transactions incur additional overhead due to off-chain coordination. The block production rate governed by IBFT 2.0 had limited impact on private transaction latency, due to encryption and peer synchronization. Lessons learned highlight design considerations for smart contract structure, validator management, and scalability patterns suitable for dynamic inter-domain collaboration. Our findings offer practical insights for deploying trustworthy agreement systems in 6G networks using privacy-enabled hybrid blockchains.

Tanapoom Sermchaiwong, Jiasi Shen

The decentralized and unregulated nature of cryptocurrencies, combined with their monetary value, has made them a vehicle for various illicit activities. One such activity is cryptojacking, an attack that uses stolen computing resources to mine cryptocurrencies without consent for profit. In-browser cryptojacking malware exploits high-performance web technologies like WebAssembly to mine cryptocurrencies directly within the browser without file downloads. Although existing methods for cryptomining detection report high accuracy and low overhead, they are often susceptible to various forms of obfuscation, and due to the limited variety of cryptomining scripts in the wild, standard code obfuscation methods present a natural and appealing solution to avoid detection. To address these limitations, we propose using instruction-level data-flow graphs to detect cryptomining behavior. Data-flow graphs offer detailed structural insights into a program's computations, making them suitable for characterizing proof-of-work algorithms, but they can be difficult to analyze due to their large size and susceptibility to noise and fragmentation under obfuscation. We present two techniques to simplify and compare data-flow graphs: (1) a graph simplification algorithm to reduce the computational burden of processing large and granular data-flow graphs while preserving local substructures; and (2) a subgraph similarity measure, the n-fragment inclusion score, based on fragment inclusion that is robust against noise and obfuscation. Using data-flow graphs as computation fingerprints, our detection framework PoT (Proof-of-Theft) was able to achieve high detection accuracy against standard obfuscations, outperforming existing detection methods. Moreover, PoT uses generic data-flow properties that can be applied to other platforms more susceptible to cryptojacking such as servers and data centers.

Abhimanyu Nag, Dhruv Bodani, Abhishek Kumar

As restaking protocols gain adoption across blockchain ecosystems, there is a need for Actively Validated Services (AVSs) to span multiple Shared Security Providers (SSPs). This leads to stake fragmentation which introduces new complications where an adversary may compromise an AVS by targeting its weakest SSP. In this paper, we formalize the Multiple SSP Problem and analyze two architectures : an isolated fragmented model called Model $\mathbb{M}$ and a shared unified model called Model $\mathbb{S}$, through a convex optimization and game-theoretic lens. We derive utility bounds, attack cost conditions, and market equilibrium that describes protocol security for both models. Our results show that while Model $\mathbb{M}$ offers deployment flexibility, it inherits lowest-cost attack vulnerabilities, whereas Model $\mathbb{S}$ achieves tighter security guarantees through single validator sets and aggregated slashing logic. We conclude with future directions of work including an incentive-compatible stake rebalancing allocation in restaking ecosystems.

Yannik Kopyciok, Friedhelm Victor, Stefan Schmid

Privacy-focused cryptocurrencies like Monero remain popular, despite increasing regulatory scrutiny that has led to their delisting from major centralized exchanges. The latter also explains the recent popularity of decentralized exchanges (DEXs) with no centralized ownership structures. These platforms typically leverage peer-to-peer (P2P) networks, promising secure and anonymous asset trading. However, questions of liability remain, and the academic literature lacks comprehensive insights into the functionality, trading activity, and privacy claims of these P2P platforms. In this paper, we provide an early systematization of the current landscape of decentralized peer-to-peer exchanges within the Monero ecosystem. We examine several recently developed DEX platforms, analyzing their popularity, functionality, architectural choices, and potential weaknesses. We further identify and report on a privacy vulnerability in the recently popularized Haveno exchange, demonstrating that certain Haveno trades could be detected, allowing transactions to be linked across the Monero and Bitcoin blockchains. We hope that our findings can nourish the discussion in the research community about more secure designs, and provide insights for regulators.

Hanners, Michael

Beal's Conjecture (Andrew Beal, 1993) is a Clay Mathematics Institute Millennium Prize problem. It generalizes Fermat's Last Theorem: the exponential Diophantine equation Ax + By = Cz with positive integers A, B, C, x, y, z and x, y, z > 2 has integer solutions only if A, B, C share a common prime factor. This manuscript presents a conditional resolution via the Harmonic Coherence (HC) framework and Hanners Theorem (HT). We translate entropy-minimization principles from HC and HT into number theory. An entropy functional H(A,B,C) is defined over the normalized terms (Ax, By, Cz). Equilibrium (gradient zero) yields pi = 1/3, which requires Ax = By = Cz. Combined with Ax + By = Cz, this implies 2Cz = Cz—impossible for positive integers. Thus no coprime solution can satisfy equilibrium; any admissible solution must share a common prime factor. The proof is supported by modular arithmetic and congruence arguments (Stewart–Tijdeman, Darmon–Granville) and by extensive computational validation (34 tests, all PASS) over large integer domains. No counterexamples were found. v5.0 changes: Fixed L1 displacement bound in Lemma coprime-displacement from ≥ 1/6 to the correctly derived ≥ 1/3. Added perturbative translation note to the A3 closure strategy (bounded-height families as amplitude cutoff). Updated documentation (test count 15 → 34, Zenodo DOI). All changes sourced from deep vector DB mining of the knowledge system. Companion documents: • Contextual Entropy Reduction Theorem • Canonical Reconciliation (Song of Coherence) • HC Bridge Note • Fixed-Point Convergence Theorem • Paper A: Transformer Distillation as Spectral Filtering • Paper B: GW Kerr Ringdown • Paper C: HC Bridge Synthesis

Atta Yaw Agyeman, Samuel Gbli Tetteh, Sofo Mohammed-Nurudeen, Lois Azupwah

Today, as digitalisation and the service economy gain prominence, the integrity of information and data has become crucial across all sectors. As a digitalised public ledger, blockchain has significant potential to enhance transparency and trust both within and across organisational boundaries. The primary objective of this essay is to bridge the gap between the theoretical understanding of blockchain and its practical application for further development and research of the technology. It also explores why it took many years for blockchain to emerge as a prominent topic and discusses the actual usability of this technology. Understanding the subject and research questions will facilitate comprehension of the discussion and the key findings of the systematic review. The main aim is to search for and analyse articles and information regarding how blockchain technology can improve transparency and trust within organisations and the associated benefits and challenges. Three research questions guide the search for relevant literature to deepen understanding and provide a comprehensive overview of the current state of the technology: “What is blockchain technology and how does it work?”, “How can blockchain be employed to achieve transparency?” and “What are the benefits and challenges of utilising blockchain technology for transparency and trust?”. Presently, the most pertinent question related to contemporary society concerns how blockchain technology can be harnessed to provide transparency. Although blockchain technology is currently in its introductory phase, it is expected to grow exponentially in the future. As the private information of individuals or organisations can be stored in the distributed network of blockchain, data security challenges are more pronounced in this context. Achieving a higher degree of transparency is an effective strategy for fostering public awareness and trust, especially in today’s data-driven society. Consequently, the significance of transparency at all levels is underscored, whether for an individual seeking transaction security and legitimacy or a governmental organisation striving for greater trust and reliability.

Felicia Abednego, Chandra Kuswoyo, Cen Lu, Christine Aliwinoto

Cryptocurrency is a digital asset accessible to the public and utilized for legal and illegal activities. While it serves as a medium of exchange and is recognized as a legal payment method in some countries, it has yet to attain such status in Indonesia. Nevertheless, by mid-2021, the number of Cryptocurrency investors in Indonesia had reached 6.5 million, with transaction values totaling 370.4 trillion rupiahs—surpassing the 5.37 million capital market investors recorded simultaneously. This study explores the profiles of Cryptocurrency investors. It also uses purposive sampling and multiple regression analysis to examine how awareness and perceived ease of use influence their motivation to adopt cryptocurrency. The findings reveal that awareness and ease of use significantly affect adoption motivation. These insights contribute to a better understanding of consumer behavior in the context of Cryptocurrency adoption. Keywords: Crypto Currency, Profile, Awareness, Ownership Motivations Abstrak Cryptocurrency adalah aset digital yang dapat diakses oleh publik dan digunakan untuk kegiatan baik secara legal dan ilegal. Meskipun berfungsi sebagai alat tukar dan diakui sebagai metode pembayaran legal di beberapa negara, masih banyak negara yang belum mengadopsi Cryptocurrency sebagai metode pembayaran ilegal salah satunya adalah Indonesia. Meski demikian, pada pertengahan 2021, jumlah investor mata uang kripto di Indonesia telah mencapai 6,5 juta, dengan nilai transaksi sebesar 370,4 triliun rupiah melampaui 5,37 juta investor pasar modal yang tercatat secara serentak. Studi ini dilakukan untuk mengeksplorasi profil investor cryptocurrency. Ini juga menggunakan purposive sampling dan analisis regresi berganda untuk memeriksa bagaimana kesadaran dan kemudahan penggunaan yang dirasakan memengaruhi motivasi mereka untuk mengadopsi cryptocurrency. Temuan ini mengungkapkan bahwa kesadaran dan kemudahan penggunaan secara signifikan mempengaruhi motivasi adopsi. Wawasan ini berkontribusi pada pemahaman yang lebih baik tentang perilaku konsumen dalam konteks adopsi mata uang kripto.Kata kunci: Cryptocurrency, Profil, Kesadaran, Motivasi Kepemilikan

Antonio Villafranca, Igor Tasic, Victor Gallegos, Almudena Giménez · 7 authors

Distributed Ledger Technologies (DLT), such as Bitcoin, Ethereum, and Directed Acyclic Graphs (DAG), are being positioned as a promising solution for smart agriculture by enabling secure, decentralized, and transparent traceability systems. However, these technologies face challenges related to scalability, latency, and efficiency in IoT environments. In this study, we conduct a comparative analysis of Bitcoin, Ethereum, and DAG technologies through extensive simulations, varying transaction generation rates and network latencies. A key methodological innovation of this research is the detailed codification of agricultural data transactions, encompassing parameters such as crop type, fertilization, harvesting, and transportation, enabling a structured and scalable approach to data representation. Our results reveal that Bitcoin's robustness is hindered by its high sensitivity to latency and network load, with inclusion times exceeding 700 s. Ethereum demonstrates better adaptability, with controlled inclusion times ranging from 12.91 to 35.76 s under varying conditions. DAG outperforms both, achieving significantly lower inclusion times between 4.27 and 22.25 s, highlighting its suitability for real-time applications. To the best of our knowledge, this is the first study to provide a direct comparison of these technologies in the context of agricultural traceability, showcasing the advantages and limitations of DAG-based systems for managing and scaling agricultural IoT networks.

Mohammadhossein Lashkaripour, Seyed Mehdi Hosseini, Elnaz Basirian, Elie Bouri

No abstract is available for this record.

Yuanhong Wu, Wei Ye, Jingyan Xu, D. Frank Hsu

In this work, we propose to apply a new model fusion and learning paradigm, known as Combinatorial Fusion Analysis (CFA), to the field of Bitcoin price prediction. Price prediction of financial product has always been a big topic in finance, as the successful prediction of the price can yield significant profit. Every machine learning model has its own strength and weakness, which hinders progress toward robust-ness. CFA has been used to enhance models by leveraging rank-score characteristic (RSC) function and cognitive diversity in the combination of a moderate set of diverse and relatively well-performed models. Our method utilizes both score and rank combinations as well as other weighted combination techniques. Key metrics such as RMSE and MAPE are used to evaluate our methodology performance. Our proposal presents a notable MAPE performance of 0.19 %. The proposed method greatly improves upon individual model performance, as well as out-performs other Bitcoin price prediction models.

WooJung Jon, Sung‐Pil Park

This study presents a comparative analysis of trademark protection in the metaverse and the registration of virtual goods and non‐fungible tokens (NFTs) across three distinct legal systems: those of the United States, the United Kingdom, and South Korea. Drawing on recent case law and evolving administrative guidelines, this study examines how traditional trademark doctrines—such as the likelihood‐of‐confusion standard in the U.S. under the Lanham Act, source-identifying function under the UK Trade Marks Act 1994, and proactive legislative reforms implemented by the Korean Intellectual Property Office—are being adapted to address the challenges posed by digital and virtual environments. Specifically, this study analyzes landmark cases such as Hermès International v. Rothschild and Yuga Labs, Inc. v. Ripps , which illustrate the extension of trademark protection to NFTs and other digital assets, as well as the interplay between trademark rights and freedom of expression. It also evaluates recent updates to international classification frameworks—including the 2024 Nice Classification and the Madrid Protocol—and discusses their implications for ensuring uniformity and effective enforcement of trademarks in a borderless digital market. The findings reveal that while each jurisdiction applies its own legal traditions to metaverse trademark disputes, all share a common policy objective: to prevent consumer confusion and safeguard brand integrity in an increasingly digital economy. Ultimately, the study advocates for proactive registration of trademarks as virtual goods and NFTs to streamline enforcement and enhance legal certainty, thereby fostering innovation and facilitating global trade in virtual environments.

Nurul Komariyatin

Objective: This research aims to explore how elements of immersive brand experiences in the metaverse-including gamification, interactive narrative, social interaction, visual realism, and the use of blockchain technology and NFTs-contribute to emotional engagement, belonging, and consumer loyalty. Research Design & Methods: This study uses an exploratory qualitative approach with in-depth interview techniques with more than 20 participants who actively interact with brands on metaverse platforms such as Roblox, Decentraland, and The Sandbox. Data analysis was conducted through a thematic approach to identify patterns of consumer engagement and perception. Findings: The results show that immersive experiences that are participatory and personalized drive strong emotional attachment to brands. Gamification increases intrinsic motivation, interactive narratives deepen the emotional experience, and social interactions form a sense of community. Realistic visualizations increase trust, while NFT ownership provides a sense of exclusivity and control that strengthens consumer loyalty. Implications & Recommendations: Brands are advised to design holistic metaverse experiences, integrating game elements, stories, communities and blockchain-based digital assets to build more meaningful and sustainable relationships with consumers. Contribution & Value Added: This research makes a theoretical contribution to the study of digital marketing and brand engagement by highlighting the importance of a multidimensional approach based on immersive technology in building consumer loyalty in the Web3 era.

Julie Saesen, Bastian Kindermann, Darius Abel, Steffen Strese

Decentralized autonomous organizations (DAOs) leverage blockchain technology to facilitate collaboration and decentralize decision-making through rules encoded in smart contracts. DAOs challenge traditional governance theory by combining ownership and management using governance tokens. A governance token’s performance is traceable based on token price changes and subject to influencing factors. Understanding these factors is essential to ensure DAOs’ long-term viability as a new organizational form. However, insights into the drivers of token performance are still limited, leaving both DAO founders and potential token holders in the dark about which on-chain governance design choices to adopt to attract potential token holders. We apply signaling theory to elucidate how on-chain governance design choices influence token performance and use a unique dataset of 204 DAOs. We find that a minimum token quorum positively relates to token performance, while embedding voting incentives, such as non-fungible tokens or additional governance tokens, can negatively relate to token performance. Moreover, we identify founders’ stake as a contingent factor moderating these signals.

Rajendra Patil, Suman Kumar Swarnkar, Deepak Yashwantrao Bhadane, Govind Mohanlal Poddar · 6 authors

Decentralized Autonomous Organizations (DAOs), which employ smart contracts and blockchain technology to facilitate decentral-ized, ‎democratic, and transparent decision-making, are an innovative change in organizational governance. This study examines the basic architecture of DAOs in the larger blockchain ecosystem, emphasizing how smart contracts help automate governance proce-dures. On-chain and ‎off-chain governance models are the main topics of this thorough analysis of DAO governance systems. Off-chain governance refers to the ‎decision-making process that happens outside the blockchain to increase flexibility and efficiency. In contrast, on-chain governance uses ‎blockchain-based voting to guarantee transparency and immutability. A comparison of these models assesses their benefits, drawbacks, and ‎compromises, especially regarding decentralization, security, and decision-making efficiency. There is also discussion of developments in DAO ‎governance, such as cross-chain interoperability, hybrid models, and AI-assisted decision-making. The results highlight the necessity of a ‎well-rounded governance strategy that incorporates both off-chain flexibility and on-chain security. By providing insights on optimizing ‎decision-making frameworks for decentralized organiza-tions, this research adds to the continuing discussion on DAO governance‎.

Saru Kumari

The rapid evolution of cryptocurrency markets, coupled with the escalating sophistication of fraudulent activities, has amplified the necessity for advanced machine learning (ML) methodologies to augment the detection, prediction, and behavioral analysis of Bitcoin transactions. Conventional approaches to fraud detection and market analysis frequently falter in capturing cryptocurrency ecosystems' intricate, dynamic, and exceedingly volatile essence. This research elucidates a data-driven framework that employs machine learning to identify scams, forecast Bitcoin market fluctuations, and scrutinize user behavior patterns within the U.S. cryptocurrency domain. By leveraging extensive Bitcoin transaction datasets enriched with features such as transaction volumes, timestamps, wallet activities, and anomaly indicators, the study deploys a diverse array of models: Random Forest, XGBoost, Logistic Regression, Support Vector Machines (SVMs), Graph Neural Networks (GNNs), Isolation Forest, and Autoencoders for fraud detection; Long Short-Term Memory (LSTM) networks and Deep Q-Learning for price prediction and trend forecasting; and K-Means clustering for the behavioral analysis of user activities. The study integrates time-series analysis, anomaly detection pipelines, and dimensionality reduction techniques to enhance predictive robustness and address challenges such as pronounced volatility, concept drift, and data sparsity. Moreover, the data imbalance issues intrinsic to fraud detection are confronted through strategic resampling methodologies. Model performance is meticulously assessed utilizing metrics such as Accuracy, Precision, Recall, F1-Score, ROC-AUC, and RMSE for forecasting endeavors.

Daniel Commey, Garth V. Crosby

Federated Learning (FL) enables collaborative model training while preserving data privacy, but its classical cryptographic underpinnings are vulnerable to quantum attacks. This vulnerability is particularly critical in sensitive domains like healthcare. This paper introduces PQS-BFL (Post-Quantum Secure Blockchain-based Federated Learning), a framework integrating post-quantum cryptography (PQC) with blockchain verification to secure FL against quantum adversaries. We employ ML-DSA-65 (a FIPS 204 standard candidate, formerly Dilithium) signatures to authenticate model updates and leverage optimized smart contracts for decentralized validation. Extensive evaluations on diverse datasets (MNIST, SVHN, HAR) demonstrate that PQS-BFL achieves efficient cryptographic operations (average PQC sign time: 0.65 ms, verify time: 0.53 ms) with a fixed signature size of 3309 Bytes. Blockchain integration incurs a manageable overhead, with average transaction times around 4.8 s and gas usage per update averaging 1.72 x 10^6 units for PQC configurations. Crucially, the cryptographic overhead relative to transaction time remains minimal (around 0.01-0.02% for PQC with blockchain), confirming that PQC performance is not the bottleneck in blockchain-based FL. The system maintains competitive model accuracy (e.g., over 98.8% for MNIST with PQC) and scales effectively, with round times showing sublinear growth with increasing client numbers. Our open-source implementation and reproducible benchmarks validate the feasibility of deploying long-term, quantum-resistant security in practical FL systems.

Nicolas Coloma, William Kleiber

Lévy processes are widely used in financial modeling due to their ability to capture discontinuities and heavy tails, which are common in high-frequency asset return data. However, parameter estimation remains a challenge when associated likelihoods are unavailable or costly to compute. We propose a fast and accurate method for Lévy parameter estimation using the neural Bayes estimation (NBE) framework -- a simulation-based, likelihood-free approach that leverages permutation-invariant neural networks to approximate Bayes estimators. We contribute new theoretical results, showing that NBE results in consistent estimators whose risk converges to the Bayes estimator under mild conditions. Moreover, through extensive simulations across several Lévy models, we show that NBE outperforms traditional methods in both accuracy and runtime, while also enabling two complementary approaches to uncertainty quantification. We illustrate our approach on a challenging high-frequency cryptocurrency return dataset, where the method captures evolving parameter dynamics and delivers reliable and interpretable inference at a fraction of the computational cost of traditional methods. NBE provides a scalable and practical solution for inference in complex financial models, enabling parameter estimation and uncertainty quantification over an entire year of data in just seconds. We additionally investigate nearly a decade of high-frequency Bitcoin returns, requiring less than one minute to estimate parameters under the proposed approach.

O. Yudina

This study considers inflationary processes and trends occurring in the modern Russian economy. The relevance of the topic under consideration is conditioned by the next round of inflationary rally, which began in 2022, and the Bank of Russia forecasts promise to slow down and stop it not earlier than 2027. The scientific analysis includes a step-by-step analysis of theoretical inflationary factors and consideration of practical aspects of such volatility in dramatic micro and macroeconomic mass-schemes. The key problems identified in the study include the lack of coordination of actions of the Government of the Russian Federation, the Ministry of Finance of the Russian Federation and the Bank of Russia. It also raises the question of the difficulties faced by financial intermediaries and economic entities in the conditions of permanent high inflation, and its extension to inflationary expectations of centralized and decentralized finance. This study aims to analyze and summarize the tight monetary factors accompanying the inertial braking path of inflation and contributing to the seamless cooling of the economy, to develop strategies aimed at targeting the Bank of Russia's stated 4%. The final results of the study substantiate the significance of the role of the Bank of Russia as a regulator of monetary policy, emphasize the conditions and main measures for the implementation of the DCP, and highlight their conceptual problems. The study is based on the analysis of foreign and Russian publications, as well as on the statistical conclusions of financial and statistical data.

Dileep Kumar Murala, Samia Loucif, Kanishka Rao, Habib Hamam

Smart contracts are changing many business areas with blockchain technology, but they still have vulnerabilities that can cause major financial losses. Because deployed smart contracts (SCs) are irreversible once deployed, fixing these vulnerabilities before deployment is critical. This research introduces a new method that combines code embedding with Generative Adversarial Networks (GANs) to find integer overflow vulnerabilities in smart contracts. Using Abstract Syntax Trees, we can vectorize the source code of smart contracts while keeping all of the important contract characteristics and going beyond what can be achieved with conventional textual or structural analysis. Synthesizing contract vector data using GANs alleviates data scarcity and facilitates source code acquisition for training our detection system. The proposed method is very good at finding vulnerabilities because it uses both GAN discriminator feedback and vector similarity measures based on cosine and correlation coefficients. Experimental results show that our GAN-based proactive analysis method achieves up to 18.1% improvement in accuracy over baseline tools such as Oyente and sFuzz.

Wenjie Li, Graciela Corral de Zubielqui, Sally Rao Hill

This study explores the evolving intersection of branding and digital assets through the lens of non-fungible tokens (NFTs), focusing on their role in shaping dynamic brand experiences. We propose a typology framework that examines how NFTs contribute to brand experience design and provides their implications for brand-consumer relationships. The research analyses five distinct NFT functions—storytelling media, identity badges, product access pass, change medallion and gamification element—and connects these roles to five types of brand experience design: brand heritage, community, product orientation, collaboration, and gamification. The findings contribute to digital branding literature by advancing the understanding of the function of digital assets within the brand experience design. This study offers a structured understanding of the value of NFTs in digital brand building by providing the roles NFTs play in brand experience. It explores the dynamic potential of brands to integrate NFTs into their strategies in the evolving Web3 environment. Finally, the industry pattern identified in this study provides insights for scholars and practitioners seeking to utilise NFTs effectively.