Blockchain Papers

Jialu Ji, Mingjun Ji, Shengzhong Ji, Lingrui Kong

No abstract is available for this record.

Samuel A. Ughili, Joshua U. Ngwuoke

<b>Abstract</b><br>Traditional electoral systems exhibit critical vulnerabilities including vote manipulation, centralized points of failure, and compromised transparency that undermine democratic integrity. This research presents BLOCKELECT, a decentralised blockchain-based secure voting system designed to address these fundamental challenges. The system employs Ethereum smart contracts written in Solidity to enforce immutable voting rules, Web3.js for blockchain integration, and MetaMask wallet authentication for secure voter verification. The proposed architecture implements dual interfaces for voters and electoral commissions, with distributed consensus mechanisms ensuring real-time transaction validation. Smart contracts automatically enforce electoral rules while maintaining cryptographic immutability of all voting transactions. The decentralised design eliminates single points of failure by distributing vote storage and validation across multiple network nodes. System validation employed comprehensive testing including unit, integration, system, and security testing methodologies. Results demonstrate successful prevention of vote tampering, elimination of double voting, and provision of transparent, auditable election results. Implementation utilised Truffle framework, Ganache blockchain simulation, and Node.js back-end services following an Agile Prototype-based Iterative Development methodology. This research demonstrates the feasibility of blockchain technology in creating trustworthy electoral systems, indicating that blockchain-based voting represents a viable solution for enhancing democratic processes while addressing persistent challenges of electoral fraud and lack of public confidence in traditional voting mechanisms.Traditional electoral systems exhibit critical vulnerabilities including vote manipulation, centralized points of failure, and compromised transparency that undermine democratic integrity. This research presents BLOCKELECT, a decentralised blockchain-based secure voting system designed to address these fundamental challenges. The system employs Ethereum smart contracts written in Solidity to enforce immutable voting rules, Web3.js for blockchain integration, and MetaMask wallet authentication for secure voter verification. The proposed architecture implements dual interfaces for voters and electoral commissions, with distributed consensus mechanisms ensuring real-time transaction validation. Smart contracts automatically enforce electoral rules while maintaining cryptographic immutability of all voting transactions. The decentralised design eliminates single points of failure by distributing vote storage and validation across multiple network nodes. System validation employed comprehensive testing including unit, integration, system, and security testing methodologies. Results demonstrate successful prevention of vote tampering, elimination of double voting, and provision of transparent, auditable election results. Implementation utilised Truffle framework, Ganache blockchain simulation, and Node.js back-end services following an Agile Prototype-based Iterative Development methodology. This research demonstrates the feasibility of blockchain technology in creating trustworthy electoral systems, indicating that blockchain-based voting represents a viable solution for enhancing democratic processes while addressing persistent challenges of electoral fraud and lack of public confidence in traditional voting mechanisms.

Caiqi Bu, Kaixia Zhang

No abstract is available for this record.

G. Gangadevi, Sudharsun Ravisankar, P Vikash

"With growing concerns about mental well-being, users want efficacious means to monitor emotions and get personalized assistance, with current solutions often sacrificing privacy or offering shallow revelations. ZenLoop overcomes the shortcomings by combining AI-based analysis of emotion with safe Web3 storage to provide both well-being support alongside privacy. This paper builds a conversational AI chatbot that offers coping mechanisms, a mood tracker to record emotion states, and an analysis dashboard to enable users to identify behavior patterns. Developed with React for frontend, Node.js for backend, and MongoDB for organized data, ZenLoop provides empathy-based responses leveraging NLP models trained on mental well-being dialogues. Journals are encrypted and stored in Web3-based storage, with immutable, decentralized protection. Trends in moods are depicted in interactive graphs, and AI-driven insights enable users to monitor emotion shifts. Tests show enhanced user engagement, improved self-perception, along with superior protection of data. The chatbot is effective in detecting levels of distress along with recommended interventions, promoting emotional resilience. By combining AI-driven tools for mental well-being with the security of blockchain, ZenLoop enables users to express emotion securely, monitor their mental well-being patterns, and get personalized advice at no cost of privacy. This work demonstrates the potential of privacy-based AI-based solutions to promote well-being at the emotional level, leading to the development of secure, user-centric applications for mental well-being.

Thomas Gassmann, Stefanos Chaliasos, Thodoris Sotiropoulos, Zhendong Su

Zero-knowledge proofs (ZKPs) are the cornerstone of programmable cryptography. They enable (1) privacy-preserving and verifiable computation across blockchains, and (2) an expanding range of off-chain applications such as credential schemes. Zero-knowledge virtual machines (zkVMs) lower the barrier by turning ZKPs into a drop-in backend for standard compilation pipelines. This lets developers write proof-generating programs in conventional languages (e.g., Rust or C++) instead of hand-crafting arithmetic circuits. However, these VMs inherit compiler infrastructures tuned for traditional architectures rather than for proof systems. In particular, standard compiler optimizations assume features that are absent in zkVMs, including cache locality, branch prediction, or instruction-level parallelism. Therefore, their impact on proof generation is questionable. We present the first systematic study of the impact of compiler optimizations on zkVMs. We evaluate 64 LLVM passes, six standard optimization levels, and an unoptimized baseline across 58 benchmarks on two RISC-V-based zkVMs (RISC Zero and SP1). While standard LLVM optimization levels do improve zkVM performance (over 40\%), their impact is far smaller than on traditional CPUs, since their decisions rely on hardware features rather than proof constraints. Guided by a fine-grained pass-level analysis, we~\emph{slightly} refine a small set of LLVM passes to be zkVM-aware, improving zkVM execution time by up to 45\% (average +4.6\% on RISC Zero, +1\% on SP1) and achieving consistent proving-time gains. Our work highlights the potential of compiler-level optimizations for zkVM performance and opens new direction for zkVM-specific passes, backends, and superoptimizers.

Adi Mutha, Jitendra Sandu

With the advent of quantum computing, cryptocurrencies that rely on blockchain technology face mounting cryptographic vulnerabilities. This paper presents a comprehensive literature review evaluating how quantum algorithms, specifically Shors and Grovers, could disrupt the foundational security mechanisms of cryptocurrencies. Shors algorithm poses a threat to public-key cryptographic schemes by enabling efficient factorization and discrete logarithm solving, thereby endangering digital signature systems. Grovers algorithm undermines hash-based functions, increasing the feasibility of fifty one percent attacks and hash collisions. By examining the internal mechanisms of major cryptocurrencies such as Bitcoin, Ethereum, Litecoin, Monero, and Zcash, this review identifies specific vulnerabilities in transaction and consensus processes. It further analyses the current hardware limitations of quantum systems and estimates when such attacks could become feasible. In anticipation, it investigates countermeasures including Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD), and protocol-level modifications such as memory-intensive proof-of-work algorithms and multi-signature schemes. The discussion integrates recent advancements in quantum error correction, hardware scalability, and NIST-standardized cryptographic algorithms. This review concludes that while quantum computers are not yet advanced enough to pose an immediate threat, proactive integration of quantum-resistant solutions is essential. The findings underscore the urgent need for cryptocurrencies to adopt post-quantum cryptographic standards to preserve the decentralized trust, integrity, and security that define blockchain-based digital cryptocurrencies.

Yang Li, Hanjie Wang, Yuanzheng Li, Jiazheng Li · 5 authors

Wind power data often suffers from missing values due to sensor faults and unstable transmission at edge sites. While federated learning enables privacy-preserving collaboration without sharing raw data, it remains vulnerable to anomalous updates and privacy leakage during parameter exchange. These challenges are amplified in open industrial environments, necessitating zero-trust mechanisms where no participant is inherently trusted. To address these challenges, this work proposes ZTFed-MAS2S, a zero-trust federated learning framework that integrates a multi-head attention-based sequence-to-sequence imputation model. ZTFed integrates verifiable differential privacy with non-interactive zero-knowledge proofs and a confidentiality and integrity verification mechanism to ensure verifiable privacy preservation and secure model parameters transmission. A dynamic trust-aware aggregation mechanism is employed, where trust is propagated over similarity graphs to enhance robustness, and communication overhead is reduced via sparsity- and quantization-based compression. MAS2S captures long-term dependencies in wind power data for accurate imputation. Extensive experiments on real-world wind farm datasets validate the superiority of ZTFed-MAS2S in both federated learning performance and missing data imputation, demonstrating its effectiveness as a secure and efficient solution for practical applications in the energy sector.

Aalok Kumar, Sourabh Kumar, Sunil Tiwari

No abstract is available for this record.

Andris lukss

The dawn of the disruptive quantum computing scenario marks a serious threat to the existence of traditional cryptosystems. With laws such as Shor’s, capable of factoring large integers in polynomial time, and Grover’s, able to speed up brute-force key searches, these attacks make conventional public-key infrastructures increasingly vulnerable, whereas even symmetric ciphers lose good measure of their strength. In this article, we focus on an elaborative description of a patented method for quantum-secure key generation, wherein Qabbalah (QBLH) complexity is utilized in the geometric-symbolic realm, in conjunction with magic number squares, phi/pi coordinate weighting, and tetrahedral trinary state encoding. The proposed system of TriGate QBLH Quantum-Safe Encryption converts seed inputs to multidimensional keys that resist linear algebraic attacks owing to non-linear permutations, irrational constant weighting, and topological complexity. Normally, pseudo-random number generators spatialize entropy in Euclidean geometry, as opposed to the present technique that places entropy in a completely non-Euclidean domain, where classical as well as quantum adversaries find it hard to traverse. We describe the method in detail, present its benefits over lattice- and hash-based post-quantum schemes, and walk through an example of its implementation. Consideration is also given to its potential integration with PQC standards, blockchain authentication, and decentralized finance applications. The system fuses symbolic mathematics, such as the 231 Gates of QBLH, with trinary logic mapped onto tetrahedral states to not only create encryption keys but also verifiable geometric signatures. This represents a paradigm shift toward geometric cryptography, which may be a viable method to realize scalable and trustworthy digital infrastructure in a quantum-threatened environment.

Authors unavailable

This synopsis presents a practical and secure online banking prototype that blends a traditional relational database with a private blockchain ledger.The goal is to improve integrity, transparency, and auditability of banking transactions without compromising usability or performance.The work starts by tracing the evolution of digital banking-from branch-led paper systems to ATMs, online and mobile banking-highlighting how centralization remains a single point of failure and a lucrative target for attackers.It then introduces blockchain as a distributed, append-only, tamper-evident ledger, and explains how its built-in cryptography and consensus mechanisms create a powerful audit trail for financial records.The proposed system anchors every transaction to a private blockchain (for immutability) while keeping sensitive customer data in an ACID-compliant PostgreSQL database (for privacy and performance).A two-password security flow-static MPIN for login and OTP for authorizing each transaction-helps reduce account takeover risk.The prototype is built with Python (Flask), uses Proof of Work (PoW) for demonstration, and includes an admin dashboard with a blockchain explorer.Testing shows responsive user interactions via asynchronous mining, and the roadmap outlines a move to permissioned consensus (PBFT/IBFT) for scale.Overall, this hybrid approach offers banks a credible, evolutionary path to stronger security, more efficient auditing, and faster reconciliation with familiar tools and user experiences. I.

Thi Thanh Thuy, Minh‐Ky Nguyen, Thuyet D. Bui, Hoang Phan Hải Yen · 10 authors

This paper explores how blockchain technology, widely known as the backbone of cryptocurrencies, can be harnessed to address limitations of traditional water quality monitoring (WQM) systems. Blockchain offers a decentralized, tamper-proof ledger that enables secure, transparent, and traceable data management across distributed networks. When applied to water quality monitoring, blockchain facilitates real-time data acquisition, enhances data integrity, and enables smart contracts for automated regulatory compliance and alerts. These features not only improve the accuracy and efficiency of WQM systems but also build public trust in the reported data. Key insights from current research and pilot applications highlight blockchain’s capacity to integrate with IoT devices for real-time sensing, support adaptive water governance, and empower local stakeholders through decentralized control and transparent access to information. The implications for policy and practice are significant: blockchain-based WQM can support stronger regulatory enforcement, encourage cross-sector collaboration, and provide a robust digital foundation for sustainable water management in smart cities and rural areas alike. As such, this review paper positions blockchain as a transformative tool in the digital transition toward more resilient and equitable water management systems.

Norchene Moumni, Faten Chaabane, Fadoua Drira

This paper proposes a blockchain framework integrating smart contracts and machine learning to enable secure, decentralized anomaly detection in energy grids. We deploy Ethereum-based smart contracts to trigger localized alerts by postcode, validated on the Ausgrid dataset. Experimental results demonstrate the framework’s ability to achieve 94.5% anomaly detection accuracy while reducing false alerts by 30% through a two-stage machine learning pipeline. First, the MeanShift clustering algorithm identifies irregular consumption patterns using adaptive interquartile range thresholds, followed by supervised classification. Various algorithms are investigated, with and without SMOTE, to tackle the problem of dataset imbalance. The Proof-of-Stake (PoS) consensus mechanism reduces energy overhead by 99% compared to traditional Proof-of-Work (PoW), ensuring scalability for real-time grid management. By automating postcode-specific alerts and leveraging blockchain’s tamper-proof data storage, the framework enhances operational responsiveness and transparency for decentralized energy systems. This work bridges the gap between decentralized ledger technology and AI-driven analytics, offering a practical solution for secure, low-latency anomaly management in modern smart grids.

Antonio Pierrò

The convergence of Artificial Intelligence (AI) and blockchain technology represents a paradigm shift in the financial industry, promising to unlock unprecedented levels of efficiency, security, and automation. This paper explores the synergistic relationship between AI and blockchain, with a particular focus on their applications within the burgeoning field of Decentralized Finance (DeFi). We analyze the technical integration of these two transformative technologies, examining how AI can enhance blockchain's capabilities and how blockchain can provide a trusted environment for AI-driven processes. The paper investigates a range of applications, including AI-enhanced trading, autonomous financial agents, and cross-border payments, while also addressing the critical challenges related to security, trust, and regulation. By synthesizing insights from a broad range of academic literature, including foundational and recent works, this paper provides a comprehensive overview of the current state of AI-blockchain integration in DeFi and identifies key areas for future research. We argue that the fusion of AI and blockchain is not merely an incremental improvement but a fundamental evolution that will reshape the future of financial markets.

Sufyan Al-Janabi

Blockchain is a Distributed Ledger Technology (DLT) that offers numerous benefits including decentralization, transparency, efficiency, and reduced costs. Hence, blockchain has been included in many fields. Blockchain relies on cryptographic protocols (especially public-key cryptography and hash functions) to achieve many essential sub-routines. However, the increased progress of quantum computation and algorithms has threatened the security of many traditional cryptosystems. Therefore, this represents a serious risk for the existing blockchain technology. For example, SHA-256 and the Elliptic Curve Digital Signature Algorithm (ECDSA) cryptosystems can be compromised by Shor s and Grover s quantum algorithms in the foreseeable future. Post-Quantum Cryptography (PQC) is a basic solution for resisting these quantum attacks. Applying PQC to blockchains results in creating Post-Quantum Blockchains (PQB). Thus, this paper aims to review the threats imposed by quantum computers on classical blockchain technology and provide useful guidelines on PQB security to blockchain researchers. The paper focuses on the challenges and opportunities of future work direction in this field.

S. V. Shitkov

The article is devoted to the analysis of key technological trends of the fourth industrial revolution and their impact on modern international re­ lations. Such areas of technological development as the Internet of Things and distributed ledger technologies are considered. Particular attention is paid to issues of digital sovereignty and the need for a balance between national regulation and international cooperation in the context of global digital transformation.

Bharat Sethi

In the scholarly literature on financial services, the prevailing paradigm over the past century was the supply of lending, borrowing, trading, and investment by regulated intermediaries, i.e. mainly banks, brokers and exchanges. These are institutions, which operate under a known regulatory structure that assumes homogeneity in their operations. Most recently, the fusion of an entirely new set of technologies such as Decentralized Finance (DeFi) has been changing the sector in a very fundamental way. Blockchain technology currently provides a framework, with the help of which financial applications can perform the transaction process automatically without the existence of central power and, thus, create an environment that is transparent and available.

Roman Serebriakov, Iryna Klymenkо

Smart contracts upgradeability is a critical requirement for modern decentralized applications based on blockchain technology, but its implementation remains a technical challenge due to the immutable nature of smart contracts. The Proxy Pattern has become the most widely used solution for implementing upgradability into smart contracts, but it comes with some limitations such as implementation complexity and strict memory layout inheritance. This paper introduces an alternative approach based on dynamic routing with Router Contract, which enables modular upgradeability through centralized address management, offering greater flexibility at the cost of requiring external state persistence. Furthermore, a hybrid architecture is proposed, combining both Proxy Pattern and Router Contract approaches to achieve dual-layer upgradeability – supporting both state-preserving updates and full module replacements. The paper offers a comprehensive evaluation of upgradeability strategies and proposes a versatile solution for evolving smart contract systems.

Tehrim Yoon, Minyoung Hwang, Eunho Yang

Modern generative models, particularly denoising diffusion probabilistic models (DDPMs), provide high-quality synthetic images, enabling users to generate diverse images and videos that are realistic. However, in a number of situations, edge devices or individual institutions may possess locally collected data that is highly sensitive and should ensure data privacy, such as in the field of healthcare and finance. Under such federated learning (FL) settings, various methods on training generative models have been studied, but most of them assume generative adversarial networks (GANs), and the algorithms are specific to GANs and not other forms of generative models such as DDPM. This paper proposes a new algorithm for training DDPMs under federated learning settings, VQ-FedDiff, which provides a personalized algorithm for training diffusion models that can generate higher-quality images FID while still keeping risk of breaching sensitive information as low as locally-trained secure models. We demonstrate that VQ-FedDiff shows state-of-the-art performance on existing federated learning of diffusion models in both IID and non-IID settings, and in benchmark photorealistic and medical image datasets. Our results show that diffusion models can efficiently learn with decentralized, sensitive data, generating high-quality images while preserving data privacy.

Andreea Borislavschi, Aida Petca

Autologous platelet-rich plasma (PRP) has emerged as a promising regenerative therapy in various medical fields, including the treatment of stress urinary incontinence (SUI) in women. PRP promotes regeneration by delivering a concentrated dose of platelets to damaged tissues, triggering healing mechanisms such as hemostasis, revascularization, and connective tissue regeneration through the release of growth factors. Despite evidence supporting the short- and medium-term benefits of PRP, its long-term efficacy remains unclear, largely due to the limited duration of follow-up in existing studies. Furthermore, the lack of standardized protocols for both preparation and administration of PRP poses a significant challenge to accurately assessing and comparing its sustained therapeutic outcomes. This literature review utilized comprehensive searches of PubMed and Google Scholar databases to analyze current evidence regarding PRP’s role in managing SUI in women. SUI, often resulting from weakened pubo-urethral ligament or intrinsic sphincter dysfunction due to childbirth, aging, or hormonal changes, significantly impacts quality of life. PRP offers a safe, minimally invasive, and cost-effective treatment option; however, further large-scale, well-designed studies are necessary to define optimal protocols and confirm long-term benefits. Advancing understanding of PRP therapy could substantially improve clinical management and patient quality of life in SUI.

Barbara Čeryová, Peter Árendáš

No abstract is available for this record.

Righa Tandon, Neeraj Sharma

No abstract is available for this record.

N. Ramanjaneya Reddy, Supriya Suryadevara, K. Guru Raghavendra Reddy, R. Umamaheswari · 6 authors

Quantum computing is an evolution of classical computing, capable of solving problems that are competitive enough to break the existing cryptographic primitives upon which current blockchain systems are based. Popular schemes like RSA, ECDSA, and SHA-256 can be compromised by quantum algorithms (Shor's and Grover's), raising questions about the security and trustworthiness of blockchain-based applications in finance, healthcare, and supply chains. Many current approaches focus on isolated aspects of the blockchain, such as cryptographic primitives or key exchange, without a comprehensive strategy that can guarantee end-to-end security in the face of a quantum threat. Finally, traditional consensus mechanisms such as Proof-of-Work and Proof-of-Stake are vulnerable to Sybil attacks, centralization, and leader-selection bias. When the adversary has access to a quantum computer, these issues become significantly worse. In this paper, we present QuantumShield-BC, a modular blockchain framework incorporating post-quantum cryptographic signatures, quantum key distribution (QKD), and a novel Quantum Byzantine Fault Tolerance (Q-BFT) consensus mechanism driven by quantum random number generation (QRNG) to address these challenges. QKD: The system supports tamper-proof key exchange, quantum-resilient consensus among validator nodes, and secure transaction signing. Experimental evaluation demonstrates that QuantumShield-BC achieves low consensus latency and high throughput, while providing perfect security against simulated attacks from Shor's and Grover's algorithms. The proposed framework eradicates the Sybil attack effectiveness up to 0%, eliminates replay and MITM vulnerabilities, and achieves an average throughput of over 7,000 transactions per second with 100 validators, orders of magnitude better than classical blockchain systems. The importance of each quantum part to the system's robustness is also demonstrated using an ablation study. With its unique ability to provide a post-quantum framework for high-assurance, general-purpose, scalable, and interoperable blockchain networks resistant to quantum-inspired attacks or quantum retrieval, QuantumShield-BC is practical for deployment in critical infrastructure and digital trust ecosystems where performance and a future-proof foundation are essential.

Alhad Daftardar, Jianqiao Mo, Joey Ah-kiow, Benedikt Bünz · 6 authors

Zero-Knowledge Proofs (ZKPs) have emerged as a powerful tool for secure and privacy-preserving computation. ZKPs enable one party to convince another of a statement's validity without revealing anything else. This capability has profound implications in many domains, including machine learning, blockchain, image authentication, and electronic voting. Despite their potential, ZKPs have seen limited deployment because of their exceptionally high computational overhead, which manifests primarily during proof generation. To mitigate these overheads, a (growing) body of researchers has proposed hardware accelerators and GPU implementations of both kernels and complete protocols. Prior art spans a wide variety of ZKP schemes that vary significantly in computational overhead, proof size, verifier cost, protocol setup, and trust. The latest and widely used ZKP protocols are intentionally designed to balance these trade-offs. One particular challenge in modern ZKP systems is supporting complex, high-degree gates using the SumCheck protocol. We address this challenge with a novel programmable accelerator to efficiently handle arbitrary custom gates via SumCheck. Our accelerator achieves upwards of $1000\times$ geomean speedup over CPU-based SumChecks across a range of gate types. We include this unit in zkPHIRE, a programmable, full-system accelerator that accelerates the HyperPlonk protocol. zkPHIRE achieves $1486\times$ geomean speedup over CPU and $11.87\times$ geomean speedup over the state-of-the-art at iso-area. Together, these results demonstrate compelling performance while scaling to large problem sizes (upwards of $2^{30}$ constraints) and maintaining small proof sizes ($4-5$ KB).

Qizhao Chen

Cryptocurrency markets are highly volatile and influenced by both price trends and market sentiment, making effective portfolio management challenging. This paper proposes a dynamic cryptocurrency portfolio strategy that integrates technical indicators and sentiment analysis to enhance investment decision-making. Market momentum is captured using the 14-day Relative Strength Index (RSI) and Simple Moving Average (SMA), while sentiment signals are extracted from news articles with VADER and further validated using the Google Gemini large language model. These signals are incorporated into expected return estimates and used in a constrained mean-variance optimization framework. Backtesting across multiple cryptocurrencies shows that the integrated approach outperforms traditional benchmarks, including momentum strategy, Bitcoin Long-Short strategy, and an equal-weighted portfolio, achieving stronger risk-adjusted returns and more consistent cumulative growth. Furthermore, comparing the sentiment-only and technical-only strategies shows that incorporating sentiment information alongside technical indicators can lead to more consistent performance gains. However, the strategies exhibit substantial drawdowns that coincide with known periods of market stress, indicating that additional risk-management components are required to improve stability.