Blockchain Papers

Haodi Wang, Danyang Zhang, Tangyu Jiang, D. Wang · 7 authors

No abstract is available for this record.

Min Zhang, Yuehua Chen, Xiyuan Fu

No abstract is available for this record.

М.К. Чижова

No abstract is available for this record.

Bruno Casella, Matthias Jakobs, Marco Aldinucci, Sebastian Buschjäger

No abstract is available for this record.

Miss Sheetal R. Billore

The rapid expansion of Artificial Intelligence (AI) across critical sectors has intensified concerns regarding transparency, accountability, fairness, and ethical compliance. Conventional governance mechanisms are primarily centralized, limiting auditability and increasing risks of bias, manipulation, and data misuse. This research proposes a decentralized governance framework integrating blockchain technology to strengthen ethical oversight in AI systems. Blockchain ensures immutability, transparency, and distributed verification of AI operations, including data usage, model updates, and decision records. Smart contracts are incorporated to automate enforcement of ethical principles such as informed consent, bias monitoring, and regulatory compliance. The framework enhances trust among stakeholders by enabling traceability across the AI lifecycle while protecting data integrity. The proposed model is applicable to domains like healthcare, finance, public administration, and autonomous systems where ethical reliability is critical. The study concludes that blockchainenabled governance provides a robust foundation for responsible and sustainable AI deployment.

Arbee Mae L. Castro, Chanelie B. Tabliga, Patricia Antonette Merecido, Anthony Vince P. Bongo · 5 authors

No abstract is available for this record.

Xiuyuan Zhao, Jingyi Liu, Ying Wang, Jiyuan Wang

Cryptocurrency markets exhibit complex microstructural dynamics characterized by high-frequency volatility bursts, rapid regime switching, and long-range temporal dependencies, which expose several limitations of existing volatility forecasting approaches. In particular, attention-based models suffer from prohibitive quadratic computational cost on long high-frequency sequences, while many recurrent architectures struggle to adapt to regime transitions, asymmetric volatility responses, and risk-aware uncertainty estimation. To address these gaps, this paper proposesCryptoMamba-SSM, a novel volatility prediction framework built upon Mamba-based state space models with linear computational complexity. CryptoMamba-SSM integrates selective memory mechanisms with structured state space representations to effectively capture critical market microstructure signals arising from liquidity shocks and sentiment transitions, while dynamically adjusting memory retention across different volatility regimes. This design enables efficient modeling of long-sequence dependencies inherent in cryptocurrency price movements without incurring the computational bottlenecks of traditional attention-based architectures. Through comprehensive experiments on Bitcoin historical data spanning multiple market regimes, we demonstrate that CryptoMamba-SSM consistently outperforms conventional LSTM, GRU, and Transformer baselines, achieving up to a 23.7% reduction in Mean Absolute Error and a 31.2% improvement in directional accuracy. The selective memory mechanism effectively captures regime-switching behaviors and microstructural anomalies, leading to more reliable short-term volatility risk quantification. Moreover, the linear-time complexity of CryptoMamba-SSM enables real-time processing of high-frequency trading data while maintaining strong generalization across diverse market conditions.

Steven Paul Nohr

<b><i>Zero-knowledge proof (ZKP) systems</i></b> such as<b><i> zk-SNARKs</i></b> and<b><i> zk-STARKs</i></b> are increasingly promoted as comprehensive solutions for privacy, scalability, and selective disclosure in blockchain-based systems. While these cryptographic primitives provide strong guarantees regarding computational correctness and data confidentiality, they are frequently mischaracterized as substitutes for regulatory compliance, legal enforceability, or supervisory control mechanisms. This paper presents a structural and functional comparison between zero-knowledge proof systems and the Crystal Validator™ (CV), a logic-layer enforcement architecture designed to encode and enforce jurisdiction?aware regulatory requirements. We demonstrate that cryptographic validity proofs are orthogonal to—and insufficient for—legal compliance, accountability, and regulatory supervision as required under frameworks such as the EU Markets in Crypto-Assets Regulation (MiCA). We argue that regulatory enforcement logic must exist above cryptographic proof layers and cannot be replaced by them without introducing systemic compliance risk.

Hong Min, Yousef Ibrahim Daradkeh, Jung Taek Seo, Mohd Anjum · 5 authors

This study presents a computational modeling framework for efficient and secure computation offloading in Internet of Things (IoT)-enabled smart contract systems. The integration of IoT, edge computing, and blockchain introduces significant challenges, including limited device capacity, high verification cost, and scalability constraints. Existing blockchain verification approaches depend on computationally intensive cryptographic operations that are inefficient for resource-constrained IoT devices, resulting in increased latency, energy consumption, and transaction costs. To address these issues, this study proposes the Zero-Knowledge Fuzzy Logic Offloading and Rollup (Z-FLOR) framework, an adaptive and energy-efficient model designed to enable secure and verifiable computation in IoT-based smart contract systems. The proposed framework integrates three key components. First, a zero-knowledge proof-based verification model using the Groth16 zkSNARK module generates compact and privacy-preserving proofs that enable fast and reliable verification. Second, a Fuzzy Logic–Driven Energy-Aware Offloading module dynamically allocates computational tasks between IoT devices, edge servers, and cloud platforms based on energy availability, network delay, and device reliability. Third, an Optimistic Rollup Verification module aggregates proofs off-chain and submits them in batches to reduce gas costs and enhance scalability. Extensive simulation and experimental evaluation across diverse IoT scenarios demonstrate the effectiveness of the proposed computational framework. Results indicate that Z-FLOR achieves 99.7% verification accuracy and 98.9% proof compression efficiency, while gas cost analysis indicates gas cost reductions in the range of 80%–98%. Z-FLOR additionally achieves a 44.0% reduction in latency, 51.0% savings in gas costs, and 38.0% energy consumption compared to baseline approaches. These findings highlight the capability of the proposed approach to serve as a scalable and energy-efficient modeling solution for secure IoT smart contract execution in decentralized environments.

Nai-Hui Chia, Kai-Min Chung, Xiao Liang, Jiahui Liu

No abstract is available for this record.

Tan Nguyen Ngo

Machine learning-based intrusion detection systems can identify malicious network activity, but their predictions and explanations are typically accepted without verifying that they were derived from the same input. This thesis develops a public-model/private-input zero-knowledge framework for certifying a prediction and its semantic explanation while keeping the processed network-flow features private. The framework is instantiated through a TON_IoT intrusion detection case study in which 104 processed features are mapped into five semantic groups. Logistic Regression is used as the proof-compatible public model, while XGBoost provides a stronger plaintext performance baseline. The main technical contribution is an implementation-backed proof relation that jointly verifies Logistic Regression inference and an ordered top-3 semantic explanation from the same private input. Under a fixed training-mean reference, semantic-group Exact SHAP for the linear score reduces to a direct group-wise weighted sum, enabling its implementation in a Circom circuit and verification using Groth16. The quantized relation achieves more than 99.99% prediction agreement with the floating-point model, while ordered top-3 explanation agreement is approximately 93.8%. Valid proofs are accepted, whereas incorrect predictions, malformed rankings, and out-of-range inputs are rejected. The results demonstrate the feasibility of cryptographically binding a prediction and a semantic explanation under private tabular inputs. The implemented relation remains limited to a public linear model, fixed semantic groups, and an approved reference vector, and does not provide arbitrary-model explanation verification, model confidentiality, or production-ready provenance.

Lukas Aumayr, Zeta Avarikioti, Matteo Maffei, Giulia Scaffino · 5 authors

No abstract is available for this record.

Hanyu Mao, Tiezheng Nie, Minghe Yu, Xiaomei Dong · 6 authors

No abstract is available for this record.

Sayan Bairagi

No abstract is available for this record.

Mohammad Muavia

No abstract is available for this record.

Prateek Sharma

No abstract is available for this record.

Ezekiel Ologunde

Modern vehicles are distributed embedded computing platforms whose expanding network connectivity-CAN bus, Bluetooth, cellular telematics, and over-the-air (OTA) update channels-exposes them to the same class of adversarial attacks studied in cloud and enterprise environments. Machine learning (ML)-based intrusion detection systems (IDS) have emerged as the primary defensive response, yet these models are themselves vulnerable to adversarial perturbation: a well-crafted malicious CAN frame can evade an ML-based IDS in the same way that an adversarial image patch fools a computer-vision classifier. This paper traces the threat landscape from foundational automotive attack-surface studies through contemporary adversarial ML research, examines how resource-constrained embedded platforms limit defensive options, and proposes a defense architecture that combines behavioral anomaly detection with zero-knowledge proof (ZKP) attestation for invehicle control units. We argue that ZKP-based component attestation-previously dismissed as computationally impractical for embedded systems-is now feasible given recent advances in succinct non-interactive arguments of knowledge (SNARKs), and that combining it with adversarially trained ML-IDS models yields defensein-depth that addresses both network-layer and hardware-layer attack vectors.

Abdul-Latif Mohammed, Ibrahim Nandom Yakubu, Abdul Jalil Mahama

No abstract is available for this record.

Natia Surmanidze, Iza Gigauri, Catalin Popescu

No abstract is available for this record.

C. Coşkun Küçüközmen

No abstract is available for this record.

Bhakti Parashar, Sandeep Kautish, Vaibhav Parashar

No abstract is available for this record.

Fredrick Mito Ogodo

Entrepreneurship, innovation, and startup ecosystems have become central components of national development strategies, particularly in Sub-Saharan Africa, where youth unemployment, income inequality, and limited formal employment opportunities remain persistent structural challenges. This paper presents a systematic review of peer-reviewed studies published between 2020 and 2025 to examine how these interconnected elements contribute to Kenya’s socioeconomic development. Guided by the entrepreneurial passion theory and the risk-bearing theory of entrepreneurship, the review synthesizes both empirical and conceptual evidence across four thematic areas: job creation and poverty reduction; financing constraints and governance weaknesses; the role and reach of innovation hubs; and human capital and skills development. The findings indicate that entrepreneurship plays a significant role in employment generation, income creation, and technological progress in Kenya. Small and medium enterprises continue to absorb a substantial share of the labour force, particularly among youth. However, the study finds that the sector’s overall contribution to national development is limited by restricted access to affordable finance, inconsistent policy implementation, weak institutional coordination, and notable skill gaps among enterprise founders. These structural challenges reduce business survival rates and limit long-term growth. The review further finds that innovation hubs, including Nairobi’s iHub and university-based incubation centres, have created valuable support structures through mentorship, networking, and access to digital infrastructure. Despite these gains, their impact remains geographically concentrated and does not adequately address the needs of entrepreneurs operating outside major urban centres. Moreover, many programs do not sufficiently respond to practical business management and financing challenges faced by early-stage enterprises. The paper concludes that achieving Kenya’s Vision 2030 development objectives requires a coordinated and sustained strategy. The study therefore recommended that the government should strengthen entrepreneurship education, expand access to blended financing, decentralize innovation infrastructure, and improve institutional coordination to promote sustainable enterprise development in Kenya

Manju Bhardwaj, Shweta Sankhwar, Ojasvi Yadav, Rinkal Bhadauria · 6 authors

Over the past decade, decentralized digital currencies have gained prominence in finance and technology, but their growth has also drawn adversaries exploiting security vulnerabilities. This paper reviews the literature on cryptocurrency and security using bibliometric analysis of Web of Science and Scopus articles published between 2013 and May 2025. Tools such as Biblioshiny and VOSviewer were employed to explore key trends, influential contributors, collaborative networks, and emerging themes. A novel contribution of this study is the use of Large Language Models (LLMs) to address inconsistent affiliation formats, enabling accurate identification of leading academic organizations. The results demonstrate that LLM-based harmonization effectively prevents misrepresentation in bibliometric datasets. Overall, this study not only summarizes evolving research trends in cryptocurrency and security but also highlights the potential of LLMs to enhance bibliometric methods, suggesting broader applications for improving the accuracy and reliability of future scholarly analyses.

Mykola Khranovskyi, Andriy Kernytskyy

The growing use of biometric authentication systems has raised serious concerns about the privacy and security of sensitive user data. Zero-Knowledge Proofs (ZKPs) provide a cryptographic solution that allows authentication without revealing the actual biometric templates. However, applying them in practice is often difficult due to the computational complexity of the circuits involved. It is commonly assumed that only simple metrics, such as Hamming distance, are suitable for these limited environments, while arithmetic metrics like Euclidean distance are considered too "heavy" or slow. This research challenges that assumption by comparing the performance of both metrics within a Groth16 Zero-Knowledge framework. For our methodology, we used a ResNet18 neural network to generate fixed-length biometric data (embeddings). To make this data compatible with the cryptographic system, we used a quantization strategy for the Euclidean metric and a binarization strategy for the Hamming metric. The experiments, conducted using the Circom compiler and snarkjs library, show very little difference in performance between the two approaches. The Euclidean circuit required 577 constraints, which is only 9% more than the 529 constraints of the Hamming circuit. Furthermore, both methods had an average proof generation time of approximately 0.5 seconds on standard hardware. These findings empirically prove that high-accuracy Euclidean comparisons can be used efficiently in Zero-Knowledge protocols. This allows developers to focus on biometric precision without sacrificing cryptographic performance.