H. P. Yu, Yinglong Gao, Shen Su, Zhen Yang · 6 authors
Decentralized storage auditing approaches are designed to ensure data security in dishonest decentralized storage providers. However, the need for data updates introduces new challenges to the design of decentralized storage auditing approaches. Existing approaches can support dynamic auditing for updated files. Unfortunately, they can only deal with block-level updating, which is counter-intuitive and requires conversion from semantic changes to binary changes. Furthermore, existing dynamic auditing approaches require the recalculation of auxiliary auditing information (e.g., auditing authenticators) in data owners, which imposes unnecessary additional burdens on data owners, particularly those with constrained resources in decentralized storage environments. In this paper, we focus on image files and propose iAudit, an efficient pixel-level dynamic image auditing approach in decentralized storage. We first design a novel image authenticator with image pixels for efficient dynamic auditing, which combines convolution operations and polynomial commitment in authenticator construction. Additionally, we build an owner-free dynamic mechanism in dynamic decentralized storage auditing approach by utilizing zero-knowledge proof techniques. In this way, the dynamic operation overheads incurred by auditing can be completely eliminated from the data owners. A prototype of iAudit is implemented, and extensive experimental results demonstrate that iAudit outperforms state-of-the-art works, achieving over a 210× speedup for data owner in dynamic update phase.
Advanced Data Storage Technologies
Cloud Data Security Solutions
Advanced Steganography and Watermarking Techniques
This study introduces a unified and methodologically symmetric comparative framework for multivariate cryptocurrency forecasting, addressing long-standing inconsistencies in prior research where model families, feature sets, and preprocessing pipelines differ across studies. Under an identical and rigorously controlled experimental setup, we benchmark six deep learning architectures—LSTM, GPT-2, Informer, Autoformer, Temporal Fusion Transformer (TFT), and a Vanilla Transformer—together with four widely used econometric models (ARIMA, VAR, GARCH, and a Random Walk baseline). All models are evaluated using a shared multivariate feature space composed of more than forty technical indicators, identical normalization procedures, harmonized sliding-window formations, and aligned temporal splits across five high-liquidity assets (BTC, ETH, XRP, XLM, and SOL). The experimental results show that transformer-based architectures consistently outperform both the recurrent baseline and classical econometric models across all assets. This superiority arises from the ability of attention mechanisms to capture long-range temporal dependencies and adaptively weight informative time steps, whereas recurrent models suffer from vanishing-gradient limitations and restricted effective memory. The best-performing deep learning models achieve MAPE values of 0.0289 (BTC, GPT-2), 0.0198 (ETH, Autoformer), 0.0418 (XRP, Informer), 0.0469 (XLM, Informer), and 0.0578 (SOL, TFT), substantially improving upon the performance of both LSTM and all econometric baselines. These findings highlight the effectiveness of attention-based architectures in modeling volatility-driven nonlinear dynamics and establish a reproducible, symmetry-preserving benchmark for future research in deep-learning-based financial forecasting.
The article systematizes modern methods of zero-knowledge proof (ZKP). Classification features are considered: protocol interactivity, algebraic or stochastic basis, need for trusted setup, type of zero-knowledge, and proof model. Classical schemes (Fiat–Shamir, Schnorr, Blum), modern zk-SNARK and zk-STARK, as well as novel approaches – PLONK, Halo 2, Bulletproofs, lattice-based ZKPs, and machine learning proofs are described. A comparative analysis is conducted according to efficiency, proof size, generation and verification complexity. It is shown that SNARKs provide compactness but require a trusted setup, while STARKs are transparent and post-quantum secure but large. Open problems are highlighted: recursive proofs, standardization, metadata protection, and applications in machine learning. It is concluded that further research in this field is aimed at creating scalable, secure, and quantum-resistant protocols for digital technologies.
Mahad Abdiwali Mohamed, Ahmed Nur Dirie, Abdiaziz Bashir Mohamud, Mohamed Abdisamad Farah · 5 authors
This paper conducts a bibliometric review of the scholarly sources of the intersection of cryptocurrencies, crowdfunding, and Islamic finance, in order to see the trends, contributions, and new directions to make Shariah-compliant FinTech and sustainability. The past decade witnessed the revolution of digital technologies such as blockchain, IoT, and AI in the banking and industries. Cryptocurrencies make the peer-to-peer transactions possible and crowdfunding helps businesses to raise funds. The concept of blockchain and central bank digital currency (CBDCs) will support sustainable finance by improving green bonds and reducing emissions. Crowdfunding in Islamic finance complies with the Shariah, as offered under risk-sharing schemes like the Mudarabah and Qard Hasan; however, the integration of the cryptocurrency as an additional risk management tool faces regulatory and compliance difficulties. Despite recent growing academic attention since 2017, most prominently dropped by Malaysia and Indonesia, there exist gaps in understanding their synergistic role towards financial inclusion and strong sustainability (SS). Blockchain relieves gharar and automates contracts that are Shariah compliant; however, there are still regulatory disagreements. Planned performing and ethics theories, the Theory of Planned Behaviors and Maqasid al-Shariah educate about the open and fair influences in adoption and assessment procedures. Through the VOSviewer and Scopus data (20142025), 158 articles reveal a maximum of publications of 2024, a high of 2020 citations, and the two countries, Malaysia (60 documents) and Indonesia as the most significant ones, with such publications as the Journal of Islamic Accounting and Business Research. Recommendations on transparent, sustainable financial ecosystems involve better blockchain-based crowdfunding, Shariah-ajority digital currencies, and better cryptocurrency determinations.
This paper examines the tensions between existing infrastructure and the need for transitional change in Dutch municipal wastewater collection and treatment. In the Netherlands, sanitation is primarily managed by public actors, with local government playing a major role. The paper demonstrates how local governments navigate these tensions and are both restricted and enabled by the current infrastructure and governance arrangements. Based on interviews, literature reviews, and analyses of statistical trends, it describes five attempts at reform in Dutch sanitation from 1980 to 2020: phosphorus removal; separating stormwater from combined sewers; water cycle companies; energy factories; and decentralized sanitation. The multi-level governance system, with decentralized infrastructure and financing, allows local governments to experiment with alternative practices, develop knowledge, and employ various interactions to mainstream innovations. However, the division of tasks in Dutch sanitation governance tends to optimize sub-systems rather than the entire system. For nationwide implementation, legislation and strong central coordination are essential. Additionally, New Public Management reinforces existing infrastructure lock-in. The paper enhances our understanding of the local government’s role in transitional change and offers insights into how the challenges of existing infrastructure can be mitigated in pursuit of sustainable wastewater solutions.
This paper investigates the Granger causality relationship in Bitcoin mining from environmental, sustainable, and miner’s financial perspectives for the period of February 2017 to January 2025. Using a time-varying Granger causality approach of Shi et al. (2018,2020), we explore how the hashrate, a measure of computational power in the Bitcoin mining process, affects energy consumption, electronic waste, and miners’ revenues. Our findings reveal that an increase in hashrate leads to a significant rise in energy use and e-waste and affects miners’ revenues. In addition, we show that mining revenue Granger causes the hashrate, suggesting economic incentives drive the network security through the hashrate. These results offer new insights for investors, policymakers, and environmental economists. • A time-varying Granger causality approach is adopted. • Higher computational power directly increases electricity demand and electronic waste. • The intensity of competition, as measured by hashrate, has a significant impact on mining profitability. • Higher mining revenues incentivise the use of greater hash power.
Tobias Kranz, Vincent Schaaf, Tobias Guggenberger, Jens Strüker
Decentralized Finance (DeFi) promises to lay ground for a more open financial system enabled by blockchain technology. Therein, stablecoins have recently gained momentum as regulated and trusted payment instruments, increasingly adopted for cross-border transactions and supported by initiatives such as the GENIUS Act in the U.S. and the European MiCAR framework. While stablecoins create the foundation of trust for linking DeFi with traditional finance, the ecosystem still depends heavily on cryptocurrency markets due to limited real-world asset integration. Existing research largely focuses on traditional securities and tradable assets, but scant attention has been paid to one of the world’s largest asset classes, real estate. To address this gap, we propose a framework for the tokenization of real estate for integration into the DeFi ecosystem. Using the Design Science Research (DSR) approach, we construct and evaluate our framework through expert interviews and smart contract simulations. The simulations validate technical feasibility and demonstrate efficiency gains, with batch transfers reducing transaction costs for portfolio purchases. Building on these evaluations, we derive design principles for the nascent field of real-world asset tokenization. These principles highlight the importance of covering the entire product range, pursuing end-to-end compliance, leveraging token standards for interoperability, and extending their functionality for efficiency and scalability. By combining regulatory, organizational, and technical perspectives, our work advances design knowledge for compliant integration of real-world assets into DeFi.
Although Large Language Models (LLM) have shown impressive performance across various domains, there is a shortage of benchmarks for systematically evaluating their in-depth understanding of specialized fields such as blockchain. This study extends the Self-Instruct methodology to introduce BLADE (Blockchain Large Language model Assessment Dataset for Evaluation), a comprehensive benchmark dataset for assessing LLM comprehension in the blockchain domain. BLADE consists of a total of 1,382 questions organized according to a systematic classification of blockchain knowledge, featuring a detailed structure with 15 main categories and 5 sub-categories for each. The benchmark covers the entire spectrum of blockchain knowledge, from its fundamentals to consensus mechanisms, architecture, smart contracts, token economy, Decentralized Finance (DeFi), NFT(Non-Fungible Token)s and digital assets, security, governance, and real-world application cases. In this research, we present a benchmark generation methodology utilizing the domain knowledge of GPT-4.5, which allowed us to create high-quality evaluation items of varying difficulty and types from expert-verified seed questions. The evaluation results of various open-source LLMs, including Qwen, DeepSeek, and Kanana, on BLADE showed that current models exhibit significant differences in their understanding of blockchain, with Qwen2.5-7B-Instruct-1M achieving the highest performance. The BLADE benchmark provides a tool for precisely evaluating and improving the blockchain comprehension of LLMs, thereby promoting the effective fusion of AI and blockchain technology and contributing to the development of more reliable decentralized systems.
In this study, we employ an NFTs news attention (NFTATT) index to measure investor attention to non-fungible tokens (NFTs) and examine its impact on the price crash risk of Bitcoin futures listed on the Chicago Mercantile Exchange. Using a sample period spanning from February 2018 to June 2023, we document a negative relationship between NFTATT and Bitcoin price crash risk. Further analysis shows that the NFTATT index has a stronger mitigating effect on price crash risk when investor interest in NFTs is at a high level. Additionally, market sentiment, as measured by the crypto fear and greed index, tends to increase the likelihood of Bitcoin price crashes.
Healthcare AI needs large, diverse datasets, yet strict privacy and governance constraints prevent raw data sharing across institutions. Federated learning (FL) mitigates this by training where data reside and exchanging only model updates, but practical deployments still face two core risks: (1) privacy leakage via gradients or updates (membership inference, gradient inversion) and (2) trust in the aggregator, a single point of failure that can drop, alter, or inject contributions undetected. We present zkFL-Health, an architecture that combines FL with zero-knowledge proofs (ZKPs) and Trusted Execution Environments (TEEs) to deliver privacy-preserving, verifiably correct collaborative training for medical AI. Clients locally train and commit their updates; the aggregator operates within a TEE to compute the global update and produces a succinct ZK proof (via Halo2/Nova) that it used exactly the committed inputs and the correct aggregation rule, without revealing any client update to the host. Verifier nodes validate the proof and record cryptographic commitments on-chain, providing an immutable audit trail and removing the need to trust any single party. We outline system and threat models tailored to healthcare, the zkFL-Health protocol, security/privacy guarantees, and a performance evaluation plan spanning accuracy, privacy risk, latency, and cost. This framework enables multi-institutional medical AI with strong confidentiality, integrity, and auditability, key properties for clinical adoption and regulatory compliance.
Blockchain technology is a game-changing invention that guarantees digital transactions on decentralized networks. The vital role that cryptography plays in guaranteeing the authenticity, confidentiality, and integrity of blockchains is examined in this paper. To secure the data on the blockchain and validate transactions, we are examining fundamental cryptographic techniques like hashing, symmetric and asymmetric encryption, and digital signatures. Furthermore, advanced cryptographic solutions that have the potential to improve privacy and scalability—such as homomorphic encryption, zero-knowledge proofs, and zk-SNARKs—are being discussed. Along with reviewing consensus techniques like proof of work and proof of stake, the paper contrasts the main blockchains, including those that are still in development, like Ethereum, Solana, and Hyperledger Fabric. Through an analysis of the advantages and disadvantages of existing cryptographic implementations, the study emphasizes the necessity for additional innovation.
Autonomous Large Language Model (LLM)-based multi-agent systems have emerged as a promising paradigm for facilitating cross-application and cross-organization collaborations. These autonomous agents often operate in trustless environments, where centralized coordination faces significant challenges, such as the inability to ensure transparent contribution measurement and equitable incentive distribution. While blockchain is frequently proposed as a decentralized coordination platform, it inherently introduces high on-chain computation costs and risks exposing sensitive execution information of the agents. Consequently, the core challenge lies in enabling auditable task execution and fair incentive distribution for autonomous LLM agents in trustless environments, while simultaneously preserving their strategic privacy and minimizing on-chain costs. To address this challenge, we propose DAO-Agent, a novel framework that integrates three key technical innovations: (1) an on-chain decentralized autonomous organization (DAO) governance mechanism for transparent coordination and immutable logging; (2) a ZKP mechanism approach that enables Shapley-based contribution measurement off-chain, and (3) a hybrid on-chain/off-chain architecture that verifies ZKP-validated contribution measurements on-chain with minimal computational overhead. We implement DAO-Agent and conduct end-to-end experiments using a crypto trading task as a case study. Experimental results demonstrate that DAO-Agent achieves up to 99.9% reduction in verification gas costs compared to naive on-chain alternatives, with constant-time verification complexity that remains stable as coalition size increases, thereby establishing a scalable foundation for agent coordination in decentralized environments.
This paper proposes an information-theoretic framework for analyzing organizational structures, with particular focus on the distinction between centralized hierarchical systems and loosely coupled, decentralized organizations. While existing theories emphasize efficiency, adaptability, or normative values such as democracy, we argue that the core structural difference lies in whether an organization is capable of generating information rather than merely transmitting or compressing it. We introduce a semi-formal model in which organizations are treated as information-processing systems composed of semi-autonomous cognitive agents. Within this framework, we show that vertically centralized management structures necessarily function as lossy information bottlenecks, whereas decentralized, loosely coupled structures enable information gain through multi-source integration and negotiation. We further propose a novel information-theoretic definition of organizational "organicness" and derive necessary structural conditions for information-generating communication. This model offers a unifying theoretical account connecting organizational theory, information theory, and social epistemology.
Reliable data availability and transparent governance are fundamental requirements for distributed edge-to-cloud systems that must operate across multiple administrative domains. Conventional cloud-centric architectures centralize control and storage, creating bottlenecks and limiting autonomous collaboration at the network edge. This paper introduces a decentralized governance and service-management framework that leverages Decentralized Autonomous Organizations (DAOs) and Decentralized Applications (DApps) to to govern and orchestrate verifiable, tamper-resistant, and continuously accessible data exchange between heterogeneous edge and cloud components. By embedding blockchain-based smart contracts within swarm-enabled edge infrastructures, the approach enables automated decision-making, auditable coordination, and fault-tolerant data sharing without relying on trusted intermediaries. The proposed OASEES framework demonstrates how DAO-driven orchestration can enhance data availability and accountability in real-world scenarios, including energy grid balancing, structural safety monitoring, and predictive maintenance of wind turbines. Results highlight that decentralized governance mechanisms enhance transparency, resilience, and trust, offering a scalable foundation for next-generation edge-to-cloud data ecosystems.
The article systematizes modern methods of zero-knowledge proof (ZKP). Classification features are considered: protocol interactivity, algebraic or stochastic basis, need for trusted setup, type of zero-knowledge, and proof model. Classical schemes (Fiat–Shamir, Schnorr, Blum), modern zk-SNARK and zk-STARK, as well as novel approaches – PLONK, Halo 2, Bulletproofs, lattice-based ZKPs, and machine learning proofs are described. A comparative analysis is conducted according to efficiency, proof size, generation and verification complexity. It is shown that SNARKs provide compactness but require a trusted setup, while STARKs are transparent and post-quantum secure but large. Open problems are highlighted: recursive proofs, standardization, metadata protection, and applications in machine learning. It is concluded that further research in this field is aimed at creating scalable, secure, and quantum-resistant protocols for digital technologies.
Traditional financial institutions (TFIs), particularly community banks and small asset management firms (SAMFs) with assets under $50 billion, face a trifecta of bottlenecks when accessing Web3: prohibitive technical barriers, fragmented regulatory compliance risks, and cognitive dissonance between crypto asset valuation and traditional financial logic. In the U.S. market, constrained by multi-agency oversight (SEC, OFAC, FinCEN), the adoption rate of Web3 access among these small TFIs remains merely 5.2% (SIFMA, 2025), far below the 37.8% penetration among large institutions with assets exceeding$500 billion. Leveraging my dual expertise in quantitative finance (CFA Level III) and Web3 multi-chain development (Uniswap V3/V4 protocol experience, daos.world multi-chain DAO incubation), this study constructs a three-dimensional synergistic theoretical framework integrating regulatory adaptation, technical simplification, and valuation migration. A low-barrier access pathway is proposed, centered on the “TradFi-Web3 Connector” system—featuring compliant wallet custody based on EIP-4337 account abstraction and a traditional finance-derived Web3 asset valuation model. Empirical validation across 8 U.S. small TFIs (4 community banks, 4 SAMFs) over an 8-month period (March–October 2025) demonstrates that this pathway reduces the average onboarding cycle from 2.8 months to 9.7 days (82.5% improvement), cuts compliance costs by 61.3% (from $95,400 to$37,300 per annum), achieves a 92.4% investment decision accuracy rate, and maintains a 100% pass rate in SEC compliance reviews with zero regulatory incidents. This research fills a critical gap in low-barrier Web3 access for resource-constrained TFIs, provides a replicable paradigm for the digital transformation of U.S. traditional finance, and empirically validates the synergy between regulatory compliance and technical innovation in cross-ecosystem integration.
During disaster response, making rapid and well-informed decisions about which areas require immediate attention can save lives. However, current coordination models often struggle with unreliable data, intentional misinformation, and the breakdown of critical communication infrastructure. A decentralized, vote-based blockchain model offers a compelling substrate for achieving this real-time, trusted coordination. This article explores a blockchain-driven approach to rapidly update a dynamic 3D crisis map based on inputs from users and local sensors. Each node submits a timestamped and geotagged vote to a public ledger, enabling agencies to visualize needs as they emerge. However, ensuring the physical authenticity of these claims demands more than cryptography alone. We propose a dual-layer architecture where mobile UAV verifiers perform physical-layer attestation and issue independent location flags to the blockchain. This dual-signature mechanism fuses immutable digital records with sensory-grounded trust. We analyze core technical and human centric challenges, ranging from spoofing and vote ambiguity to verifier compromise and connectivity loss, and outline layered mitigation strategies and future research directions. As a concrete instantiation, we present a UAV mapping scheme leveraging modulated retro-reflector (MRR) sensors and 3D-aware LoS placement to maximize verifiability under urban occlusion, offering a path toward resilient, trust-anchored crisis coordination.
In this paper, we introduce Sark, a reference architecture for transferring unforgeable, stateful, oblivious (USO) assets. We describe the motivation, design, and implementation of the core subsystems of Sark, Porters, which accumulate and roll-up commitments from Clients, and Sloop, a permissioned, crash fault-tolerant (CFT) blockchain system. We analyse the operation of the system using the `CIA Triad': Confidentiality, Availability, and Integrity. We then introduce the concept of \textit{local centrality} and use it to address design trade-offs related to decentralization. Finally, we point to future work on Byzantine fault-tolerance (BFT), and mitigating the local centrality of Porters.
While Ethereum has successfully achieved dynamic availability together with safety, a fundamental delay remains between transaction execution and immutable finality. In Ethereum's current Gasper protocol, this latency is on the order of 15 minutes, exposing the network to ex ante reorganization attacks, enabling MEV extraction, and limiting the efficiency of economic settlement. These limitations have motivated a growing body of work on Speedy Secure Finality (SSF), which aims to minimize confirmation latency without weakening formal security guarantees. This paper surveys the state of the art in fast finality protocol design. We introduce the core theoretical primitives underlying this space, including reorganization resilience and the generalized sleepy model, and trace their development from Goldfish to RLMD-GHOST. We then analyze the communication and aggregation bottlenecks faced by single-slot finality protocols in large validator settings. Finally, we survey the 3-slot finality (3SF) protocol as a practical synthesis that balances fast finality with the engineering constraints of the Ethereum network.
Most current assessments use ex post proxies that miss uncertainty and fail to consistently capture the rapid change in bitcoin mining. We introduce a unified, ex ante statistical model that derives expected return, downside risk, and upside potential profit from the first principles of mining: Each hash is a Bernoulli trial with a Bitcoin block difficulty-based success probability. The model yields closed-form expected revenue per hash-rate unit, risk metrics in different scenarios, and upside-profit probabilities for different fleet sizes. Empirical calibration closely matches previously reported observations, yielding a unified, faithful quantification across hardware, pools, and operating conditions. This foundation enables more reliable analysis of mining impacts and behavior.
Rapid advancements in quantum computing and machine learning threaten the long-term security of classical blockchain systems, whose protection mechanisms largely rely on computational difficulties. In this study, we propose a quantum blockchain protocol whose protection mechanism is directly derived from quantum mechanical principles. The protocol combines high-dimensional Bell states, time-entanglement, entanglement switching, and high-dimensional superdense coding. Encoding classical block information into time-delimited qudit states allows block identity and data verification to be implemented through the causal sequencing of quantum measurements instead of cryptographic hash functions. High-dimensional coding increases the information capacity per quantum carrier and improves noise resistance. Time-entanglement provides distributed authentication, non-repudiation, and tamper detection across the blockchain. Each block derives its own public-private key pair directly from the observed quantum correlations by performing high-dimensional Bell state measurements in successive time steps. Because these keys are dependent on the time ordering of measurements, attempts to alter block data or disrupt the protocol's timing structure inevitably affect the reconstructed correlations and are revealed during validation. Recent advances in the creation and detection of high-dimensional time-slice entanglement demonstrate that the necessary quantum resources are compatible with emerging quantum communication platforms. Taken together, these considerations suggest that the proposed framework can be evaluated as a viable and scalable candidate for quantum-secure blockchain architectures in future quantum network environments.
Accurate and interpretable forecasting of multivariate time series is crucial for understanding the complex dynamics of cryptocurrency markets in digital asset systems. Advanced deep learning methodologies, particularly Transformer-based and MLP-based architectures, have achieved competitive predictive performance in cryptocurrency forecasting tasks. However, cryptocurrency data is inherently composed of long-term socio-economic trends and local high-frequency speculative oscillations. Existing deep learning-based 'black-box' models fail to effectively decouple these composite dynamics or provide the interpretability needed for trustworthy financial decision-making. To overcome these limitations, we propose DecoKAN, an interpretable forecasting framework that integrates multi-level Discrete Wavelet Transform (DWT) for decoupling and hierarchical signal decomposition with Kolmogorov-Arnold Network (KAN) mixers for transparent and interpretable nonlinear modeling. The DWT component decomposes complex cryptocurrency time series into distinct frequency components, enabling frequency-specific analysis, while KAN mixers provide intrinsically interpretable spline-based mappings within each decomposed subseries. Furthermore, interpretability is enhanced through a symbolic analysis pipeline involving sparsification, pruning, and symbolization, which produces concise analytical expressions offering symbolic representations of the learned patterns. Extensive experiments demonstrate that DecoKAN achieves the lowest average Mean Squared Error on all tested real-world cryptocurrency datasets (BTC, ETH, XMR), consistently outperforming a comprehensive suite of competitive state-of-the-art baselines. These results validate DecoKAN's potential to bridge the gap between predictive accuracy and model transparency, advancing trustworthy decision support within complex cryptocurrency markets.
Blockchain technology enables tamper-resistant and transparent data management, but it continues to face pressing challenges related to security and performance. Existing blockchain applications predominantly rely on software-based implementations, which are susceptible to side-channel analysis (SCA) attacks and constrained by the limited efficiency of general-purpose processors. This thesis addresses these challenges by leveraging Field-Programmable Gate Array (FPGA) technology to develop hardware-based solutions that strengthen cryptographic security and accelerate blockchain execution. Three major hardware contributions are presented: an Ethereum hardware wallet resistant to SCA, a hybrid Ethereum–Bitcoin hardware wallet supporting both Hierarchical Deterministic (HD) and Non-Deterministic (ND) modes, and a hardware-accelerated Ethereum Virtual Machine (EVM). The first contribution, EthVault, introduces the first complete hardware architecture of an Ethereum HD cold wallet and its FPGA implementation. EthVault integrates a side-channel resistant elliptic curve cryptography (ECC) design, the first hardware realization of the child key derivation (CKD) function, and resource-conscious implementations of key cryptographic algorithms, including ECDSA, HMAC-SHA-512, PBKDF2, SECP256K1, elliptic curve point operations, and the Ethereum checksum algorithm, resulting in a secure, compact wallet. The second contribution, HardVault, presents the first FPGA-based hybrid Ethereum–Bitcoin cold wallet architecture. Supporting both HD and ND key generation methods, HardVault improves resource efficiency by reusing cryptographic primitives common to Ethereum and Bitcoin, including RIPEMD-160, CKD, and SHA-256. This reuse minimizes hardware overhead, enabling a lightweight and energy-efficient solution. A detailed power–performance evaluation further demonstrates HardVault’s superior efficiency, with measurable improvements over commercial wallets such as Trezor One. The third contribution, EVMx, proposes a single-core FPGA-based EVM that offloads smart contract execution from full and archival nodes to a dedicated hardware accelerator. EVMx preserves full compatibility with the EVM’s stack-based semantics while introducing performance optimizations such as lightweight pipelining, simplified opcode decoding, dynamic corner-case handling, and selective parallelism. Experimental results show significant speedups for both individual opcodes and complete smart contract execution compared to CPU-based and prior FPGA designs. Furthermore, integration strategies are discussed to enable scalable adoption of EVMx within existing Ethereum clients. Overall, this thesis demonstrates that FPGA-based designs can substantially strengthen blockchain ecosystems by enhancing both security (EthVault and HardVault) and computational performance (EVMx), thereby paving the way for more secure, efficient, and practical blockchain deployments.
Cryptographic Implementations and Security
Cryptography and Residue Arithmetic
Physical Unclonable Functions (PUFs) and Hardware Security
Industry 4.0 technologies are accelerating the digital transformation of financial systems, reshaping money, payment infrastructures, and the strategic role of central banks. This study examines the emergence of Central Bank Digital Currencies (CBDCs) within this evolving landscape, exploring the evolution of payment systems, fintech integration, and the implications of distributed ledger technology and private cryptocurrencies. Using qualitative content analysis of secondary data, the paper compares the approaches of the U.S. Federal Reserve, the Bank of England, and the South African Reserve Bank to CBDC design, adoption, and regulation. Findings highlight shared policy concerns including cybersecurity, privacy, regulatory gaps, financial inclusion, and the need for international interoperability while revealing notable differences in institutional priorities and pace of development. The study underscores that central banks stand at a pivotal moment: their responses to Industry 4.0 innovations and digital currency initiatives will shape future monetary stability and the global financial order.