Blockchain Papers

Janaka Ishan Senarathna

No abstract is available for this record.

R. Femimol, L. Nalini Joseph

No abstract is available for this record.

Ihsan H. Hussein

Newborn misidentification poses serious patient safety and accountability problems, but errors can be traced through the use of a blockchain to create an audit trail. However, a blockchain storing raw or even hashed biometric templates for individual identities is not acceptable for privacy reasons. This work redefines our prior work (1) to form a privacy-preserving audit protocol that isolates the processes of capturing a biometric and matching it against a database of known identities to an external Service Provider and the processing of the blockchain to a permissioned Ledger that contains only pseudonymous audit commitments related to keyed entries on the Ledger. This work describes an implementation of this protocol in Solidity 0.8.30 and provides metrics for the gas use and latency of the smart contract for 100 iterations of 100 total Enrollment and Verification Workflows each. Twenty Adversarial Functional Tests are also described that attempt to place the system into an invalid state, as well as four additional tests that assess the effect of batched submission to the smart contract of multiple keyed audit commitments. The smart contract processing throughput is also determined for a batch of submissions, finding a maximum local throughput of 60.2 tx/s. A further 50,000 randomized reference-model transitions of the system’s internal reference-model were then made (involving a total of 57,345,087 invariant checks, all of which passed), as well as a measurement of the time taken to generate an HMAC-SHA-256-sized commitment for 10,000 iterations (local median time = 0.002 ms). The results of this work provide a solid foundation for the blockchain component of BIBIS, but it is not intended to provide any insights into the accuracy of neonatal biometric matching, the presentation attack resistance of the system, or even the usability of BIBIS by clinical end-users. The results also do not comment on the finality of QBFT-based commits to a blockchain.

Yash Chhunchha

No abstract is available for this record.

Mustafa Özyeşil, Havane Tembelo

This paper seeks to develop an empirically tested theoretical model that measures the block chain related awareness, confidence, and perceived relevance regarding finances among employees in the Turkish financial services sector. From the existing literature on blockchain adoption, the acceptance of fintech, and trust-based investment behavior, the authors developed an initial item pool consisting of 14 items. Content validation was done through experts followed by a pilot. Primary data was collected from 450 finance professionals working in the banking, treasury, risk, and accounting departments of different companies within Istanbul. The questionnaire was filled out by the respondents during the period March to April 2025 and was distributed online. Internal consistency was calculated using Cronbach’s alpha coefficient, while the structure of the underlying scale was investigated by Principal component analysis with oblique rotation. This analysis was complemented with item analysis through corrected item-total correlations and calculation of communalities. The data quality for conducting factor analysis were validated by KMO and Bartlett’s test of sphericity. From the results of the two-factor solution, the total variance explained was 85.83%. The first factor covered perceptions pertaining to blockchain awareness and informational engagement while the second predominately covered confidence in blockchains financial functionality and trustworthiness. The final structure is comprised of 14 items that have high loadings and little redundancy. The results indicate that the scale is not only clear-cut conceptually and statistically, but also provides a consistent measure for further studies regarding the perception and acceptance of technology in the finance domain.

B. Shaji, Ram Mohan N. R., K L Nisha

As the digital landscape expands, centralised cybersecurity frameworks grow increasingly vulnerable to sophisticated threats, creating single points of failure and targets for adversarial data manipulation. While AI enables real-time threat detection and big data analytics, its centralised deployment limits efficacy and exposes training data to poisoning and evasion attacks. To address this, the AICyber-Chain model proposes a distributed framework combining parallel AI and blockchain architectures. It leverages a hybrid Proof-of-Stake (PoS) and Byzantine Fault Tolerance (BFT) mechanism with IPFS and Private Data Centres (PDCs) for secure decentralised storage and processing. Generative Adversarial Networks (GANs) refine security rules, while Ethereum-based smart contracts enable automated responses and trustless data sharing. Results on the Rinkeby test network show 1.8× faster authentication, 25% lower gas consumption, F1 score of 0.92, and 1.2 s response time, with a medical data sharing use case ensuring data provenance and tamper-proof control.

Lee Yen Chaw, Eugene Cheng-Xi Aw, Mehrbakhsh Nilashi, Mcxin Tee

No abstract is available for this record.

Snehankita Majalekar

As the growth of the FinTech platforms continues, there is an increasing demand for intelligent, secure and traceable solutions that can provide real-time detection of fraudulent transactions and shield financial records from manipulation. In this research, an Artificial Intelligence-powered blockchain framework, combining machine learning for fraud detection and permissioned blockchain for validation, was proposed. It was found that ensemble models performed better than a linear baseline. The overall best balance of precision, specificity and F1 score was obtained with the Random Forest model, and the highest precision–recall was obtained with the Extra Trees model, with fraud recall slightly better. In addition, feature-importance analysis revealed a small number of transaction attributes, which were anonymised, that most significantly affected fraud classification. The chosen model was then connected to a prototype of a chained hash blockchain that preserved the hashes of transactions, the time, the predicted probability of fraud, the validation result, and the version of the model. Through hash inconsistency, the prototype was able to detect any transaction modifications which might have been made on purpose and successfully ensured ledger integrity. The results illustrate how both AI and blockchain technologies complement each other. AI is effective in detecting fraud accurately and on time, and blockchain enhances the traceability, auditability and tamper resistance of transactions.

Abhisake Sinha, Sukanta Chakraborty, Abhishek Majumder

In today's era, securing data related to environmental and forest department has become a crucial aspect. Integrating blockchain based technologies with the forest data management is an effective solution. Blockchain technology such as Hyperledger Fabric is a permissioned blockchain. It can be helpful in storing the forest data in a more accurate, secure and tamper-proof manner. In this work a Hyperledger Fabric based framework has been developed to store the forest data in a secure and cost effective manner. The developed system can be used in the forest department to improve security and to monitor forest activities like illegal logging. In the developed system all the records are stored in an immutable manner. Its decentralized and permissioned nature helps to prevent any unauthorized participants, which means it only allows the participants that are authorized or permitted to perform within the network. The proposed technique has been implemented on Fabric 2.5 Test-Network. Performance analysis revels that the proposed system can be implemented in real time.

Inayatulloh Inayatulloh

No abstract is available for this record.

Aleksandra Trpkov, Danica Sovtić, Miloš Radenković, S. Popović · 5 authors

No abstract is available for this record.

Olusegun Gbolade

The rapid digitalization of healthcare has led to the generation of vast amounts of sensitive patient information, increasing the need for advanced security solutions beyond traditional centralized systems. This study examines the integration of Artificial Intelligence (AI) and blockchain technology as a transformative approach to healthcare data security. Conventional electronic health record systems often face challenges such as single points of failure, limited transparency, and vulnerability to cyber threats. Blockchain addresses these issues by providing a decentralized and immutable ledger that ensures data integrity, traceability, and secure record management through cryptographic techniques and consensus protocols. In parallel, AI strengthens security by enabling intelligent threat detection, predictive analytics, and adaptive authentication mechanisms. Machine learning algorithms continuously analyze network activities and user behaviors to identify potential breaches and insider threats in real time. The combination of AI and blockchain creates a synergistic framework in which AI enhances blockchain efficiency, while blockchain provides a transparent and trustworthy environment for AI-driven data processing. The study further explores the role of blockchain-secured federated learning, which enables collaborative model training across healthcare institutions without exposing sensitive patient data. Key challenges, including interoperability, scalability, regulatory compliance, and integration with legacy systems, are also discussed. Additionally, patient empowerment is enhanced through self-sovereign identity models that grant individuals greater control over their personal health information. Despite challenges related to computational complexity and standardization, the convergence of AI and blockchain offers a proactive, resilient, and privacy-preserving security architecture for modern healthcare. Future research should focus on lightweight cryptographic solutions, quantum-resistant security mechanisms, and governance frameworks for decentralized healthcare ecosystems. Overall, this integration represents a significant step toward secure, transparent, and patient-centered digital healthcare systems.

Ashutosh Bhardwaj

The mushrooming digitalization of industries has heightened the need to have secure and resilient system design as well as smart system design that is prone to address intricate cyber threats and data breaches. Artificial Intelligence (AI) and Blockchain have become the new influential technological innovations that could contribute greatly to the security, visibility, and reassurances of any digital ecosystem. AI can be used to provide smart threat detection and predictive analytics, intelligent decision-making, and blockchain can be employed to provide decentralization trust, immutability, and secure data sharing. The chapter discusses the prospects of AI and blockchain in the design of secure digital architectures and how the two can be used concurrently to enhance cybersecurity, keep data intact, and ensure operational resilience. It talks about architectural structures, practical implementation in industries, and ethical or regulatory implications as well as the future opportunities to create a solid and reliable digital systems in a more globalized world.

Rakesh Kumar

The multiplication of digital infrastructures and cloud services and of cyber-physical systems connected with each other has resulted in the significant growth of cybersecurity challenges. Traditional security mechanisms are usually not qualified to process large quantities of security data and identify complex cyber threats in real-time. The combination of artificial intelligence (AI), parallel computing, and blockchain technologies show a promising way forward on creating secure, scalable and resilient cybersecurity frameworks for distributed digital infrastructures. This Chapter puts forth a conceptual framework that incorporates parallel computing architectures and block-chain enabled security mechanisms in order to increase the efficiency and trustworthiness of AI-enabled Cybersecurity systems. Parallel computing helps high-performance processing of big amounts of network traffic and security logs, so that AI models can perform better analysis of picker threat patterns and find out anomalies more efficiently.

Xiaocan Wang, Shixuan Guan, Tong Yang, Xiapu Luo · 6 authors

Cryptocurrency users have increasingly become targets of phishing and scam attacks. To mitigate these threats, leading crypto wallets (e.g., MetaMask) have introduced transaction simulation, which previews a transaction's balance changes before on-chain execution. While effective against traditional fund-draining attacks, we show that this defense can itself be exploited by a new phishing technique, which we term transaction simulation phishing. This attack uses carefully crafted smart contracts whose execution depends on dynamic blockchain state, causing simulations to display benign or profitable outcomes while the actual on-chain execution redirects users' funds to attacker-controlled addresses. We present the first comprehensive study of transaction simulation phishing. We first develop a taxonomy of phishing contracts that can be utilized to facilitate this attack. Then, we propose SIMGUARD, a bytecode-level detection system that combines static and dynamic program analysis to identify phishing contracts. Applying SIMGUARD to Ethereum, Binance Smart Chain, Avalanche, and Polygon, we detect over 4,000 phishing contracts deployed between August 2024 and June 2025. Our analysis identifies more than 5,700 victims and approximately $3.48 million USD in losses, 91.5% of which occurred on Ethereum. Moreover, our clustering result reveals that the largest phishing contract cluster alone accounts for about 83% of the total losses. These results expose a critical weakness in current wallet defenses and highlight the urgent need for more robust transaction simulation mechanisms.

Peter Bro Miltersen

Automated market makers exchange assets through liquidity pools whose quoted prices depend on their reserves, with constant product pools being the most common. When such pools reside on different blockchains or shards, a sequence of swaps cannot in general be executed atomically. Aanes et al. introduced lock-swaps and retroactive constant product liquidity pools to provide price guarantees for such a setting. A retroactive pool implicitly maintains a virtual pool for each possible execute/cancel resolution of its active locks. In the presence of active locks, serving a new swap request requires computing a safe quote; a quote with an output that does not exceed the minimum possible output, taken over all virtual pools. The quote being safe is a hard constraint ensuring the integrity of the pool. A soft constraint is to make the quote as close to the minimum possible output as possible. Aanes et al. gave a simple and efficient algorithm for computing the exact minimum when unresolved provides and reclaims of liquidity do not coexist, showed by an explicit example that the algorithm fails in general, and left the computational complexity of the general case open. In this paper, we show that unless P is equal to NP, there is no polynomial time algorithm that computes in the general case a safe quote with any fixed multiplicative approximation ratio (e.g., 50%) relative to the exact minimum. This seems like a severe obstacle for deployment of the lock-swap functionality. However, we also present two simple and practical algorithms for computing safe quotes that have input-dependent approximation ratios that are likely to be satisfactory in practice, thus circumventing that obstacle.

Yanzheng Jin, Pengyang Shao, Xiaohao Liu, Xi Ai · 6 authors

Financial trading relies on extracting reliable signals from heterogeneous market modalities such as price series, breaking news, and investor sentiment. Existing multimodal methods primarily combine heterogeneous modalities to exploit complementarity, treating each modality as equally valuable while overlooking whether different modalities provide mutually supportive evidence for the same trading signal. However, this task-conditioned and non-canceling support, termed multimodal corroboration, is particularly valuable, especially for financial trading. Because individual financial views are noisy and weakly informative, support that persists across heterogeneous views may provide a more stable task-relevant signal than evidence appearing in only one view. To exploit this property, we propose CoLAS (multimodal Corroboration of Latent Asset Signals), a framework that operationalizes multimodal corroboration as a trainable task-conditioned representation for trading prediction. The modality representations are organized into a per-instance matrix, where a softmax-based spectral objective strengthens its dominant shared component. Signed modality contributions then determine whether this component provides non-canceling support and construct the resulting corroborated signal. A coupled robustness-aware consistency objective further preserves the resulting corroborated signal when a modality is corrupted or missing. Extensive experiments on stock and cryptocurrency datasets demonstrate the effectiveness of our proposed CoLAS, yielding consistent improvements in both annualized return and Sharpe ratio over existing methods.

Xiaoye Zheng, Yujing Chen, Minghao Wu, David Lo · 8 authors

Solana is an emerging blockchain platform designed for high throughput and low transaction fees, making it inexpensive to submit transactions at scale and, consequently, increasing exposure to bot spamming and related financial exploitation. Solana bots are typically off-chain software systems that operate in a competitive on-chain execution environment by constructing and submitting transactions, and the bot-related transactions on the decentralized exchanges exceed 250 million dollars in daily trading volume in January 2026. Prior studies on Solana have examined system performance, smart-contract security, and specific on-chain phenomena. However, we still lack a systematic understanding of what Solana bots implement in practice and how these implementations manifest as observable on-chain execution fingerprints. To address this gap, we performed a large-scale empirical study of Solana bots from two complementary views: (i) 586 bot repositories collected from GitHub, and (ii) 200 bot addresses on Solana, with over 44 million on-chain transactions. Our study derives an implementation-grounded taxonomy of Solana bots comprising 15 categories grouped into five domains (e.g., Trading Operations, MEV, and On-chain Analytics), identifies a largely shared five-stage operational pipeline manifested in bot implementations, and uncovers systematic variation in on-chain trading behaviors of Solana bots across diverse trading platforms and assets. Based on our findings, we highlight future research directions, and provide recommendations for building and operating bots on the Solana blockchain.

Giuseppe Cavaliere, Thomas Mikosch, Anders Rahbek, Frederik Vilandt

This paper develops bootstrap inference for autoregressive conditional duration (ACD) models observed over a fixed calendar span, so that the number of durations is random. We study recursive schemes that either fix the calendar span or the realized event count. For the fixed-count bootstrap, we establish consistency when the duration tail index satisfies $κ\geq1$. When $0<κ<1$, classical consistency fails because the estimator has a mixed-normal limit, but the bootstrap reproduces its conditional Gaussian component. Consequently, basic percentile intervals remain first-order valid and bootstrap $t$-statistics are asymptotically standard normal. Monte Carlo experiments show accurate finite-sample inference across finite- and infinite-mean regimes and robustness to non-exponential innovations. An application to cryptocurrency ETF transaction durations finds strong persistence and illustrates the practical difference between fixed-count and random-count inference.

Niamjit Das

This chapter examines mangrove governance as a pathway for bridging policy and practice in climate resilience. It explores national and local structures, highlighting strengths and weaknesses in policies, decentralization challenges, and institutional fragmentation. Case studies from Bangladesh, Indonesia, Kenya, and the Philippines illustrate diverse approaches, from disaster risk reduction and large-scale restoration to community stewardship and carbon finance. Persistent barriers include conflicts between development and conservation, weak enforcement, equity gaps, and political-economic complexities. Future directions emphasize resilient governance systems, multi-level and transboundary cooperation, scaling community-based models, and embedding mangroves into sustainability agendas. Governance is reaffirmed as the cornerstone of sustainable mangrove management, requiring adaptive, inclusive, and collaborative frameworks to ensure ecological resilience and social equity.

Jorge San Jose, Daniel Field, UST

This article presents the DigInTraCE Blockchain Module, a secure and scalable framework for managing Digital Product Passports (DPPs) and traceability data across industrial supply chains. Built on Hyperledger Fabric, the solution combines distributed ledger technology, cloud-native infrastructure, smart contracts, and standardized EPCIS 2.0 traceability to enable trusted collaboration among multiple stakeholders. The technical article describes the platform architecture, governance mechanisms, secure API integration, identity management, and blockchain-based validation processes that support transparent, interoperable, and auditable product lifecycle information. The proposed framework provides a robust foundation for future Digital Product Passport implementations and circular industrial value chains.

Owen Marshall

No abstract is available for this record.

Emmanuel Trélat, Xingwu Zeng, Can Zhang

We establish global exponential turnpike properties for quadratic optimal tracking problems governed by the one-dimensional viscous Burgers equation with localized internal control. For every initial datum, finite-horizon optimal solutions approach the unique optimal periodic regime when the periodic tracking target is sufficiently small; the zero-target case yields a global steady turnpike at the origin, with no smallness assumption on the initial datum. To our knowledge, these are the first global exponential turnpike results for the viscous Burgers equation. The proof combines a local exponential turnpike, obtained through strict convexity and periodic Riccati theory, with a parabolic dissipation argument that provides an absorbing time independent of the horizon.

Veerpal Kaur, Devershi Pallavi Bhatt, Sumegh Tharewal, Pradeep Kumar Tiwari

To managing identities in a secure and decentralized manner, new opportunities have emerged because of recent breakthroughs in blockchain technology and biometric authentication. Blockchain is different from traditional biometric systems in that it is an unchangeable, distributed ledger that runs safe, decentralized code. Traditional biometric systems store data in one location and can’t be updated. Traditional biometric systems have some flaws, including template tampering, channel interception, and comparator overrides. So, the proposed work presents a Distributed Multimodal Biometric Security System with Blockchain to handle such issues. This system uses 3D face and 3D ear biometrics with blockchain technology, which comprises IPFS, smart contracts, and decentralized applications. Features from 3D face and 3D ear are embedded into a single multimodal template, which then undergoes encryption and storage on IPFS via content-addressed storage. The Content Identifier (CID) and data are then archived by smart contracts on the blockchain to maintain data integrity, security, verifiability, and immutability. In this way, a person can prove his identity without using any central services, further improving privacy. Blockchain consensus and the smart-contract-based access control mechanism further provide security, audibility, and simplicity to P2P transactions in biometric enrolment testing results show that feature extraction takes from 120 ms to 300 ms, uploading to IPFS takes between 200 and 600 ms, and completing blockchain transactions on local private network takes from 0.5 to 1 s, using 117,519 gas per enrolment. Additional analysis on the Ethereum Sepolia test network reveals that transaction fees change depending on network conditions, but gas consumption stays deterministic. The suggested solution is resistant to typical attacks like replay, interception, and template alteration; it is also irreversible, revocable, and unlinkable, according to security analysis conducted under a formal adversarial model.