Blockchain Papers

Yafeng Li, Wenzheng Duan, Xiaolong Jin, Lichuan Ma

No abstract is available for this record.

Jinwen Liang, Jiannong Cao, Bo Yang, Dongbin Bai · 6 authors

Decentralized Physical Infrastructure Networks (DePIN) represent a paradigm shift in infrastructure deployment and resource coordination, leveraging blockchain and token-based incentives to transform traditional service models. This paper presents a comprehensive survey of DePIN, encompassing its evolution, architecture, open issues, and practical implementation. We begin by tracing the development of DePIN across three key phases: the emergence of fungible tokens, the rise of non-fungible tokens (NFTs), and the shift toward real-world asset tokenization. We highlight representative DePIN projects and propose a unified four-layer architecture comprising the infrastructure, decentralized data, resource control, and application layers, each delivering essential functionality for scalable, interoperable DePIN systems. We then analyze open research challenges within each layer and outline promising future research directions. To demonstrate the viability of our architecture, we present DCEAI, a prototype DePIN platform that enables decentralized LLM inference via edge computing. We evaluate its performance and discuss opportunities for further enhancement. This survey aims to establish a solid foundation for future research and innovation in DePIN ecosystems.

Ghislain Nkamdjin Njike, Anh-Tu Hoang, Stefan Schulte

Distributed learning (DL) is gaining popularity as it enables clients (e.g., AI Agents) to enhance their machine learning (ML) models’ performance by exchanging knowledge without revealing private datasets. State-of-the-art DL approaches primarily focus on transferring knowledge between heterogeneous clients with diverse model architectures, connecting clients with those that can improve their models, and protecting data privacy. However, they overlook the threat of malicious clients that potentially downgrade the models’ performance by sharing inaccurate knowledge or excluding high-performing clients from the training procedure.Therefore, we introduce the Zero-Knowledge Blockchain-Based Knowledge Distillation Learning Framework (zkBKD). In zkBKD, heterogeneous clients communicate with a blockchain network to discover high-performing clients, verify zero-knowledge proofs (ZKPs) to ensure the correctness of the knowledge shared from other clients, and vote to eliminate malicious clients. We analyze security and privacy risks and show that zkBKD prevents membership, poisoning, and collusion attacks. We conduct extensive experiments on two standard datasets across heterogeneous clients with four model architectures. The experimental results demonstrate that zkBKD relatively improves the average model accuracy of all clients by 25.71%. Even lightweight models such as ResNet-2 achieve up to a 103.35% accuracy gain compared to independent training.

Nikhil Kumar, Richa Richa, Sweeti Sah, Shweta Sharma · 5 authors

Blockchain is known for being a decentralized ledger with distributed storage. It has changed whole industries across borders by increasing security, transparency, and reliance on intermediaries. Thus, from its initial design for cryptocurrencies like Bitcoin, Blockchain extends its transformative potential for a wide range of fields such as finance, supply chain management, and healthcare. The contribution of this research is an in-depth analysis of blocks, transactions, and consensus mechanisms constituting the anatomy of a blockchain. We have paid particular attention to block-structure research in our work, emphasizing that a block is an indivisible information unit, each containing transactional data and cryptographic hashes that link it to the previous block. One of the most valuable parts of our research consists of an innovative analysis of a consensus mechanism. We explain how different algorithms ensure the validity and sequence of transactions among the network nodes, and review the strengths and weaknesses of algorithms, namely, Proof of Work (PoW), Proof of Stake (PoS), and Delegated Proof of Stake (DPoS). The key highlights in this work are the case studies of well-established blockchain platforms, including Bitcoin and Ethereum. These manifest our insight into their operational efficiencies and mechanisms for security. Further, we demonstrate empirical results on the processing times for transactions and scalabilities of blockchains under different network conditions. Additionally, the challenges of scalability and energy consumption are put forth, for which novel approaches may be proposed for future blockchain development. The study contributes to the further development of blockchain technology by informing future research directions toward solving the existing limitations and exploring new applications within emergent sectors.

Thai Hong Le, Dat Thanh Pham, Khanh Dien Le, Anh Le · 5 authors

No abstract is available for this record.

Dingsen Shi, Chris Tsu, Ying He, Alex Goss · 8 authors

Zero-Knowledge Proofs (ZKPs) are becoming a foundational technology for scalable and privacy-preserving blockchain systems, especially through applications like zkRollups. However, the computational intensity of proof generation continues to limit real-world deployment. We present ZKPU, a hardware-software co-designed ZK accelerator that combines native NVMe integration—ensuring seamless compatibility across existing server and edge infrastructure—with a modular RISC-V System-on-Chip (SoC) architecture that opens the path to eliminating host–device communication bottlenecks. ZKPU is designed to flexibly support a wide range of ZK workloads; in this work, we demonstrate its capabilities by implementing and optimizing multi-scalar multiplication (MSM), a core bottleneck in many zk-SNARK systems. Built using the Chipyard framework and equipped with dedicated modular arithmetic units, ZKPU achieves significant performance and energy efficiency improvements over CPU, GPU, and FPGA baselines. Our results highlight ZKPU as a practical and forward-compatible foundation for scalable ZK acceleration in modern decentralized systems.

Tuan-Dung Tran, Dinh Khang Nguyen, Quang Trung Do, Van-Hau Pham

Cross-chain bridges, while critical for interoperability in the Web3 ecosystem, have become a primary target for exploits, accounting for over $4.3 billion in losses—nearly 40% of all value stolen in recent years. Existing security paradigms fail to address this threat adequately due to a fundamental trade-off between pre-deployment static analysis and real-time dynamic monitoring. Current security paradigms are trapped in a critical trade-off: pre-deployment static analysis lacks runtime context and suffers from high false-positives, while real-time dynamic monitoring is blind to the underlying source-code vulnerabilities that enable sophisticated attacks. This paper introduces VeriBridge, a novel framework that breaks this impasse. VeriBridge pioneers a synergistic fusion of static intelligence and dynamic graph learning. It enriches real-time transaction graphs with fine-grained vulnerability data extracted from static analysis, providing crucial security context to an unsupervised Graph Autoencoder. By learning a high-fidelity model of normal behavior, VeriBridge detects malicious transactions, including zero-day exploits, as significant deviations from this learned norm, identified by high reconstruction error. Evaluated on a comprehensive dataset of real-world attacks, including the Poly Network and THORchain exploits, VeriBridge achieves a 96% F1-score, demonstrating a new frontier in robust, real-time security for critical blockchain infrastructure.

Yuqi Zhang, Jiashuo Zhang, Ting Zhang, Jianbo Gao · 5 authors

No abstract is available for this record.

Shivakumar M, Rakshitha N, Ruqsar, Sahana M · 5 authors

In today's hyperconnected digital environment, authentication and security are paramount. As technology evolves, traditional methods of authentication such as usernames and passwords have proven increasingly vulnerable to cyberattacks, phishing scams, and identity theft. This has led to a growing need for a more secure, decentralized, and tamper-proof system to safeguard digital identities. This paper titled “NextGen Security: A secure and Decentralized authentication protocol using Non- transferable Blockchain-based Tokens” addresses this concern by proposing an innovative framework that leverages blockchain technology to implement a robust, distributed authentication mechanism. This paper envisions a future where authentication is not controlled by a centralized authority, but is instead managed through a distributed ledger. Blockchain, with its decentralized and immutable nature, ensures that user credentials and identity records are stored securely across multiple nodes, eliminating the single point of failure problem that plagues traditional systems. The proposed framework integrates Ethereum-based smart contracts, Keccak-256 (SHA- 3) hashing, and non-transferable Soulbound Tokens (SBTs) to ensure secure and decentralized identity verification. The system leverages wallet-based authentication through MetaMask and Web3.py, enabling cryptographically verifiable and tamper- proof login events on the blockchain.

М. А. Luschakov, Т. S. Oleinikova, I.N. Tsygulev

No abstract is available for this record.

Lukas Weidener, Benjamin Heurich, Bence Lukács

Abstract Decentralized Autonomous Organizations (DAOs) promise to transform governance through blockchain-enabled transparency and communal decision-making. However, unresolved legal responsibilities and speculative governance token dynamics complicate their ability to maintain trust, encourage participation, and secure legitimacy. Drawing primarily on Social Capital Theory (SCT), this study shows how bonding, bridging, and linking social capital intersect with liability ambiguities and token concentration to undermine institutional confidence and grassroots engagement. Public Goods Theory (PGT) clarifies how free-rider tendencies can deter infrastructural support, while Principal-Agent Theory (PAT) highlights incentive misalignments when whales prioritize short-term gains over collective welfare. Through a theoretical lens, this study illuminates how token-based power asymmetries, a lack of regulatory clarity, and conflicting motivations strain the viability of DAOs in fulfilling the promise of decentralized governance. In synthesizing these frameworks, this study advocates tailored governance strategies, ranging from reputation-based voting models to legally compliant organizational wrappers, to mitigate power imbalances, foster inclusive decision-making, and ultimately strengthen DAOs’ resilience in an evolving blockchain ecosystem. By bridging the sociological, economic, and legal perspectives, this study illustrates the interplay of trust, accountability, and incentives that shape DAO sustainability.

Shwetha K R, Divya G S, Bhavan Pande, Darshan K · 6 authors

Due to the ever-increasing demand to use safe and reliable electronic votes, a blockchain-based secure voting system has been developed to enhance transparency, trustfulness, and voter recognition. This system eliminates such issues as voting fraud, impersonation, and manipulating the results by means of biometric verification and decentralized blockchain ledger. The voters are matched to a facial-recognition database containing previously registered voters before voting. It is authenticated by a K-Nearest Neighbors (KNN) approach as it works well on classifying facial features and is not very laborious. After the vote is successfully authenticated, it is stored and signed on a blockchain network where it cannot be altered by another party. The features of smart contracts ensure the safety of voting, the correct counting of votes, and the awareness of each network node of what is happening. The cryptography of hashing and decentralized make certain that the votes are immutable, due to the decentralized structure of blockchain and consensus mechanisms. The face-matching module ensures that only the qualified individuals are allowed to vote. The system also supports mass elections and guarantees the ease of interaction among the voters. It was designed in such a way that it is scalable and user friendly. Trust, security, and efficiency are enhanced in the system through biometrical authentication, distributed ledger technology, encryption, and classification through machine-learning. It is highly dependable in how to conduct the current digital elections.

Wiwit Prawitri, Laras Angelia Nnirwan, Elman Azizov

This research explores the implementation of a blockchain-based forensic audit framework designed to enhance the detection and investigation of suspicious financial activities within decentralized finance (DeFi) ecosystems. The main problem addressed in this study concerns the inefficiency, lack of transparency, and vulnerability to data manipulation commonly found in traditional forensic auditing systems. The objective is to develop a model that integrates blockchain technology with graph-based anomaly detection to improve accuracy, transparency, and scalability in financial audits. The proposed method combines blockchain’s immutable ledger capabilities with automated detection algorithms and Chain of Custody (CoC) verification to ensure data integrity and accountability. The results demonstrate that the proposed system achieves a detection accuracy exceeding 90%, as presented in Table 1, and effectively categorizes different suspicious transaction patterns illustrated in Figure 2. Compared to conventional methods, the framework offers superior performance in terms of speed, reliability, and adaptability. The findings suggest that this approach establishes a new paradigm in forensic auditing by combining automation, transparency, and scalability into a cohesive analytical model. In conclusion, the study confirms that blockchain-based forensic auditing significantly enhances digital financial oversight and provides a foundation for developing intelligent, tamper-proof audit systems suitable for the evolving landscape of decentralized finance.

Harsh Rudrawar, Sumitra A. Jakhete, Dhanashree Somani

Decentralized Finance (DeFi) has exposed users to sophisticated attacks (flash loans, rug pulls, phishing scams), while conventional, centralized fraud detection methods remain ineffective due to issues of privacy, data fragmentation, and lack of transparency. This paper introduces a novel hybrid framework for real-time fraud detection combining off-chain Machine Learning (ML) intelligence with on-chain smart contract enforcement via decentralized Chainlink oracles. Unlike existing ML systems that only flag behavior post-event, our design enables proactive mitigation, allowing smart contracts to automatically pause suspicious transactions or block malicious wallets based on real-time risk scores. Using an Ethereum dataset ($\approx 1.2$million transactions), the proposed model achieved an F1-score of 95.70% with XGBoost, outperforming traditional algorithms. The framework also demonstrated efficient operation, maintaining an average oracle latency of 1.25 seconds and an on-chain cost of 0.0041 ETH per action. Future work will explore privacy-preserving ML, cross-chain detection, and DAOgoverned explainable AI to improve transparency and trust in DeFi ecosystems.

Michael Lustenberger, Florian Spychiger, Lukas Küng

No abstract is available for this record.

Barış Cantürk

Abstract The socio-economic developments and the volume of Decentralized Autonomous Organizations (“DAO”) are increasing day by day. However, debates in the field of law regarding the DAOs are still vigorous. One of the most crucial issues pertaining to DAOs is liability, which is related to their legal nature. Hence, this work first briefly reveals the current liability regime of DAOs within the context of the current landscape of German and Turkish Company Law. Particularly ordinary partnerships, joint-stock companies and limited companies will be examined. Then, the new liability regime for DAOs will be proposed, as a part of the recommendation of a “New Code”. Finally, this work will be concluded with the outcomes and recommendations.

Stefano Franco, Angelo Presenza, Antonio Messeni Petruzzelli, Myung Ja Kim

How hotel chains are using immersive technologies (ITs), in particular the metaverse, remains an overlooked issue. This work aims at showing how ITs allow companies to create and capture value, integrating it into business models. Applying a qualitative method based on case studies, interviews, and observations, results show the mechanisms through which companies can integrate ITs in their business models to create and capture value. We found that these mechanisms are related to two areas, such as customer engagement and resource management. Metaverse can improve customer engagement by working as a means that allows pre-stay visualization, enriching tourism experience, MICE and events remote planning, new customer segment engagement, non-fungible tokens (NFTs) exploitation for loyalty programs, and events selling. It also works as a means to improve resource management by facilitating employee recruitment and training, integrating metaverse experiences, exploring novel offerings, and capturing web3 potentialities.

Arpit Jain, Swati Gupta, Meenu Vijarania, Pratyush Srivastav

Security and user experience are critical priorities in modern banking, yet traditional authentication practicessuch as passwords and one-time passwords (OTPs)-remain vulnerable to phishing, credential theft, and data breaches. This research explores the integration of blockchain-based authentication, specifically using MetaMask for passwordless login, as a secure alternative for digital banking systems. The proposed approach eliminates centralized credential storage by leveraging cryptographic signatures and client-side verification, thereby enhancing both security and user privacy. A prototype banking application was developed and evaluated, demonstrating a 50 % reduction in authentication time and improved resistance to phishing attacks. The study further analyzes decentralized identity management's implications for regulatory compliance, including GDPR and KYC alignment. By comparing conventional and Web3 authentication systems, this work illustrates how decentralized login mechanisms can significantly strengthen banking security, streamline user interaction, and promote a transparent, customer-centric digital banking ecosystem.

Christopher G. Harris

Redactable blockchains enable controlled removal or modification of data to meet regulatory demands, but existing solutions often sacrifice decentralization or auditability. This paper presents a redactable blockchain architecture that combines a Redaction Policy Engine (RPE), multi-party validator voting, and post-quantum chameleon hash functions. We introduce a structured lifecycle—from submission and policy validation to execution and logging—anchored by cryptographic enforcement and on-chain governance. Our design supports GDPR-aligned features such as audit trails, user appeals, and purpose limitation enforcement. Implemented on a permissioned Ethereum network, our system demonstrates lower latency (1.82s), gas cost (128.5k), and storage overhead (1.2%) compared to prior solutions. A detailed security and compliance analysis confirms resilience against validator collusion and quantum threats. This work offers a practical framework for deploying redactable blockchains in regulated environments while preserving verifiability and decentralization.

Showkot Hossain, Wenyi Tang, Changhao Chenli, Haijian Sun · 8 authors

Healthcare data sharing is fundamental for advancing medical research and enhancing patient care, yet it faces significant challenges in privacy, data ownership, and interoperability due to fragmented data silos across institutions and strict regulations (e.g., GDPR, HIPAA). Patients possess distributed records across multiple hospitals, each maintaining autonomous databases. Access to consolidated records by secondary entities mandates explicit patient consent while ensuring strict isolation between multi-tenant datasets, requiring fine-grained access control across organizational boundaries. Existing solutions exhibit critical limitations: blockchain-based databases lack robust fine-grained cryptographic enforcement of dynamic access policies, while TEE-enhanced systems suffer from synchronization overhead and poor scalability in distributed deployments. To bridge these gaps, we propose MtDB, a novel decentralized database architecture addressing secure data sharing in multi-tenant database ecosystems. MtDB employs blockchain for metadata coordination and sharing, IPFS for distributed data addressing, a universal SQL query interface for data access, and Intel SGX for integrity-protected query execution with enforced access control. We provide an open-source implementation demonstrating MtDB’s capabilities for secure, patient-centric healthcare data sharing while preserving ownership and enforcing policies. Experimental results show MtDB achieves 35 milliseconds query latency for indexed queries over 400M multi-tenant medical records while maintaining cryptographic security guarantees, with only 1.2–1.3× performance overhead compared to non-secure baselines.

Pavel Hubáček, Jan Václavek, Michelle Yeo

The rising importance of cryptocurrencies as financial assets pushed their applicability from an object of speculation closer to standard financial instruments such as loans. In this work, we initiate the study of secure protocols that enable fiat-denominated loans collateralized by cryptocurrencies such as Bitcoin. We provide limited-custodial protocols for such loans relying only on trusted arbitration and provide their game-theoretical analysis. We also highlight various interesting directions for future research.

Hasan Akgul, Mari Eplik, Javier Rojas, Aina Binti Abdullah · 5 authors

ZK-SenseLM is a secure and auditable wireless sensing framework that pairs a large-model encoder for Wi-Fi channel state information (and optionally mmWave radar or RFID) with a policy-grounded decision layer and end-to-end zero-knowledge proofs of inference. The encoder uses masked spectral pretraining with phase-consistency regularization, plus a light cross-modal alignment that ties RF features to compact, human-interpretable policy tokens. To reduce unsafe actions under distribution shift, we add a calibrated selective-abstention head; the chosen risk-coverage operating point is registered and bound into the proof. We implement a four-stage proving pipeline: (C1) feature sanity and commitment, (C2) threshold and version binding, (C3) time-window binding, and (C4) PLONK-style proofs that the quantized network, given the committed window, produced the logged action and confidence. Micro-batched proving amortizes cost across adjacent windows, and a gateway option offloads proofs from low-power devices. The system integrates with differentially private federated learning and on-device personalization without weakening verifiability: model hashes and the registered threshold are part of each public statement. Across activity, presence or intrusion, respiratory proxy, and RF fingerprinting tasks, ZK-SenseLM improves macro-F1 and calibration, yields favorable coverage-risk curves under perturbations, and rejects tamper and replay with compact proofs and fast verification.

Kaya Alpturer, Kushal Babel, Aditya Saraf

Optimistic responsiveness -- the ability of a consensus protocol to operate at the speed of the network -- is widely used in consensus protocol design to optimize latency and throughput. However, blockchain applications incentivize validators to play timing games by strategically delaying their proposals, since increased block time correlates with greater rewards. Consequently, it may appear that responsiveness (even under optimistic conditions) is impossible in blockchain protocols. In this work, we develop a model of timing games in responsive consensus protocols and find a prisoner's dilemma structure, where cooperation (proposing promptly) is in the validators' best interest, but individual incentives encourage validators to delay proposals selfishly. To attain desirable equilibria, we introduce dynamic block rewards that decrease with round time to explicitly incentivize faster proposals. Delays are measured through a voting mechanism, where other validators vote on the current leader's round time. By carefully setting the protocol parameters, the voting mechanism allows validators to coordinate and reach the cooperative equilibrium, benefiting all through a higher rate-of-reward. Thus, instead of responsiveness being an unattainable property due to timing games, we show that responsiveness itself can promote faster block proposals. One consequence of moving from a static to dynamic block reward is that validator utilities become more sensitive to latency, worsening the gap between the best- and worst-connected validators. Our analysis shows, however, that this effect is minor in both theoretical latency models and simulations based on real-world networks.

Zhuo Liu

This paper proposes Atomic Ownership Blockchains (AOB), a novel blockchain architecture designed to address scalability and decentralization challenges in distributed ledger systems. AOB introduces an approach where each atomic object is represented by an independent blockchain, potentially allowing for horizontal scaling and enhanced security. The system stores only ownership transfer records, which may enable parallel transaction processing and improved throughput. By eliminating traditional mining and voting mechanisms, AOB aims to mitigate certain security risks while proposing an implicit consensus mechanism for resolving forks. The AOB architecture could potentially support the digitization of real-world assets and enable decentralized applications involving shared or fractional ownership. This paper presents the theoretical framework of AOB, discussing its potential advantages and outlining areas for future research and empirical validation. Practical implementation and rigorous testing are necessary to fully assess its viability and impact on digital ownership paradigms.