Blockchain Papers

Yuan sheng Tang, Youshui Lu, Rui Li, Haitao Xu · 6 authors

No abstract is available for this record.

Jonas Gehrlein, Grzegorz Miebs, Matteo Brunelli, Adam Mielniczuk · 5 authors

We consider a problem arising in proof-of-stake blockchain environments, where agents called nominators select validators - entities responsible for maintaining the blockchain's physical infrastructure. The selection process is inherently subjective and multi-criterial and combines with the fact that nominators commonly operate through multiple accounts. This gives rise to a portfolio selection problem, where agents seek to distribute their nominations across accounts to diversify risk. We propose a decision support framework to optimize this selection by simultaneously maximizing two objectives: the expected utility of the validators likely to be allocated, representing portfolio quality and profitability, and the expected entropy of the allocation, representing diversification and risk mitigation across stashes. Validator utilities are derived using an original active preference learning procedure based on multi-attribute value theory, with emphasis on top-ranked validators. The resulting bi-objective optimization problem is solved with a multi-objective evolutionary algorithm and, to support the final choice, we introduce an interactive binary search navigation procedure that guides the nominator through the front and identifies a satisfactory trade-off with only a few questions. Numerical experiments examine the optimization strategies, while an expert assessment involving five experienced nominators confirms the approach's practical relevance and usefulness.

Yixuan Fan, Lei Zhang, Yao Sun, Xinyi Lin · 5 authors

The development of blockchain technology and the emergence of Web3 have given rise to a new paradigm known as Decentralized Autonomous Organizations (DAOs), online communities jointly owned and managed by members working for the same interests. Voting is the primary decision-making method within DAOs aligning with the decentralization philosophy. However, existing DAO voting mechanisms often exhibit a strong tendency toward centralized control, contradicting DAOs’ pursuit of decentralization. In the absence of a decentralization standard, we define the decentralization coefficient as a novel metric to evaluate the overall decentralization performance of DAO voting mechanisms quantitatively by establishing the first stochastic process model for the DAO voting process. By analyzing and simulating four typical voting mechanisms, we uncover that quorum and voting power thresholds, often thought to improve voting performance, may negatively impact decentralization. Additionally, the study analyzes the impact of three main factors, including voting power distribution, participation rate, and voting process, on decentralization. The findings highlight that decentralization is shaped by the interplay between these factors, rather than merely by adopting specific voting rules. This study provides a quantitative benchmark for future research and offers practical guidance for DAO designers, emphasizing the need to prioritize inclusive participation over additional voting conditions.

Xiang Chen, Kan Lu, Alpamis Kutlimuratov

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Omar D. Eldesouky

Consensus protocols underpin the security and correctness of decentralized blockchain systems by enabling mutually untrusted participants to agree on a shared state. Early blockchain networks relied on Proof-of-Work (PoW), which achieves strong security by making participation computationally expensive, but its high energy consumption has driven the transition toward Proof-of-Stake (PoS) and its variants. Nominated Proof-of-Stake (NPoS) addresses PoS centralization tendencies by separating stake from block production, allowing participants (known as nominators) to delegate their staked tokens to validators, aiming to enable broader, fairer, and more decentralized participation in consensus. This thesis addresses the gap between NPoS’s design intentions and its realworld effectiveness by using Polkadot as a case study: we employ multi-year on-chain data and a graph-based methodology that models the evolving relationships between nominators and validators, and introduce metrics to evaluate centralization, fairness, and inclusiveness in NPoS systems. The analysis reveals persistent structural concentration. Validator turnover is limited, with around 90% of validators remaining active across consecutive eras, and nomination patterns are highly persistent, with fewer than 20 nominators controlling up to one-third of the active validator set. Regarding fairness, while validator rewards converge to near-equality, nominator rewards remain highly unequal as an inherent consequence of stake-proportional allocation. Regarding inclusiveness, participation remains constrained: a non-trivial fraction of active validators charge 100% commission, retaining all staking rewards and distributing nothing to their nominators, which constitutes a structural barrier to meaningful economic participation for the nominators backing them. Overall, these findings indicate that NPoS still exhibits concentration dynamics that limit its effectiveness in achieving decentralization and inclusiveness.

Istiaque Ahmed, Zubaer Mahmood Zubraj, Md Sadek Ferdous, Tadashi Nakano · 5 authors

Decentralized Autonomous Organizations (DAOs) suffer from critical governance challenges, such as low voter participation, large token holders’ dominance, and inefficient proposal analysis by manual processes. We propose APOLLO (Autonomous Predictive On-Chain Learning Orchestrator), an AI-powered approach that automates the governance lifecycle in order to address these problems. The gemma-3-4b Large Language Model (LLM) in conjunction with Retrieval-Augmented Generation (RAG) powers APOLLO’s multi-agent system, which enhances contextual comprehension of proposals. The system enhances governance by merging real-time on-chain and off-chain data, ensuring adaptive decision-making. Automated proposal writing, logistic regression-based approval probability prediction, and real-time vote outcome analysis with contextual feature-based confidence scores are some of the major advancements. LLM is used to draft proposals and a feedback loop to enrich its knowledge base, reducing whale dominance and voter apathy with a transparent, bias-resistant system. This work demonstrates the revolutionary potential of AI in promoting decentralized governance, paving the way for more effective, inclusive, and dynamic DAO systems.

Tony Tan, Andrian Andrian, Fredian Simanjuntak

Traditional lucky draw systems often face trust issues due to a lack of transparency and potential for manipulation. This study addresses these challenges by developing and evaluating a blockchain-based lucky draw prototype named "LuckyDraw." A mixed-method approach was employed, combining an applied research method with the Agile Scrum framework for system development, and a quantitative survey to evaluate user acceptance. The quantitative analysis, using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique on data from 100 respondents, confirmed the instrument's validity and reliability. The results showed that Perceived Usefulness (PU) was the strongest predictor of Behavioral Intention (BIU), followed by Trust (TRT). Furthermore, Perceived Ease of Use (PEOU) had a significant positive effect on PU. These findings indicate that a transparent, trustworthy, and easy-to-use system is highly accepted by users, offering a viable solution to the shortcomings of traditional systems.

Yihan Xia, Taotao Wang, Wenxin Xu, Shengli Zhang

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.

Tuan-Dung Tran, Bao Huynh, Tra Minh Trong, Tong Thuan Nguyen · 6 authors

Permissioned blockchains using Proof-ofAuthority (PoA) deliver high throughput but face issues of predictability and centralization, while token-weighted governance risks plutocracy that undermines fairness. This paper proposes Proof-of-Merit (PoM), a consensus and governance framework that integrates PoA with Verifiable Random Functions (VRFs) and a dual-token model. PoM selects validators through a weighted combination of transferable stake (UIT-Coin) and non-transferable academic reputation (UIT-Rep), earned via verifiable onchain learning activities. Governance follows the same principle, anchoring voting rights in Sybil-resistant merit rather than pure capital. To ensure sustainability, PoM introduces reputation decay, preventing long-term power concentration and promoting continuous participation. We implement PoM on Hyperledger Besu and evaluate it with Hyperledger Caliper. Results show PoM achieves strong performance while significantly improving fairness, with a much lower Gini coefficient and higher Nakamoto coefficient compared to IBFT 2.0. Sensitivity analysis further highlights the need for dynamic reputation mechanisms to avoid saturation. These contributions establish PoM as a scalable, equitable, and sustainable foundation for Learn-to-Earn ecosystems, where influence derives from ongoing educational engagement instead of wealth accumulation.

Jiayong Chai, Jun Guo, Muhua Wei, Mo Chen · 5 authors

Blockchain systems have been widely adopted in today’s society, with consensus algorithms serving as their core component to ensure all participants in the network agree on a specific data state. Existing consensus algorithms such as Proof of Work (PoW), Proof of Stake (PoS), and the Practical Byzantine Fault-Tolerant Algorithm (PBFT) exhibit certain limitations in terms of scalability, security, and efficiency. To address these limitations, this paper proposes a novel Network-based Reputation Consensus (NRC) algorithm. The main research contributions of this work include the following: (1) An intelligent grouping mechanism that dynamically groups nodes based on network awareness, forming consensus groups with low internal latency and high bandwidth utilization, significantly reducing intra-group communication overhead. (2) A dynamic reputation system incorporating a “diminishing returns” reward function and a “multiplicative penalty” mechanism, effectively incentivizing honest node participation while preventing power monopoly. (3) A two-phase model of “intra-group BFT consensus + global communication committee ordering” that decomposes complex global consensus into parallel intra-group processing and coordination among a small set of elite nodes, thereby drastically improving efficiency. (4) Comprehensive simulations comparing the NRC algorithm with mainstream consensus algorithms, demonstrating its superior performance in communication overhead, throughput, latency, and tolerance to malicious nodes, thereby laying the foundation for large-scale applications.

Hussein Othman, Fatima Abdallah, Mubarak Mohammad

In recent years, the adoption of the Internet of Things (IoT) has spread rapidly in various fields, enhancing technology and facilitating human life. To improve operational efficiency, user experience, and predictive maintenance of IoT applications, dynamic device recruitment and service provisioning are adopted instead of manual and fixed architectures. This raises challenges in adaptability, trust, and security, especially in heterogeneous environments such as smart cities. This paper utilizes blockchain and smart contracts to automate the process of IoT device recruitment and service provisioning. It proposes a decentralized architecture including a trust authority component for authentication, a smart contract generation component, and a negotiation component for dynamic contract refinement. This ensures a seamless and secure communication between the service provider and requester without reliance on third-party and manual intervention. The architecture components are presented in detail, and the full process from registration and authorization to dynamic contract generation and negotiation is then explained. It shows how manual intervention is reduced while ensuring trust and adaptability. Future work includes model formalization, prototype implementation, and performance evaluation.

Quanbi Feng, Yinan Mi, Hanzheng Lyu, Jianbin Zou · 5 authors

Decentralization is a critical security property for blockchain systems. Ethereum adopts a protocol design with multiple incentive mechanisms to encourage validators to contribute to decentralization. However, little empirical evidence exists on the actual effectiveness of Ethereum's incentive mechanism. In this paper, we collect and analyze data on validator rewards from Ethereum's consensus and execution layers, examining both the distribution of rewards and the degree of decentralization in the current network. Our findings show that Ethereum's reward allocation exhibits a relatively balanced distribution, with neither staking pools nor exchanges earning disproportionately higher returns simply due to their larger stake. These findings reveal the effectiveness of Ethereum's incentive design and the current state of decentralization, providing a foundation for future improvements in mechanism design and exploration.

Yaxian Dong, Zijun Zhan, Daniel Mawunyo Doe, Zhu Han · 5 authors

In the project-oriented construction industry, recruiting qualified workers who can finish the required tasks in a limited time is important. However, the high turnover rate in the construction workforce poses a challenge in verifying applicant information, leading to potential issues like information falsification and inaccurate assessments due to information asymmetry. Additionally, the industry’s male-dominated nature may foster stereotype-based biases, particularly concerning sensitive attributes (e.g., gender). Such situations contribute to unfair competition among applicants. The construction industry is also experiencing new technologies like BIM, AI, and Blockchain. Their integration shows potential for automation, fairness, information security, and trustworthiness in recruitment. To build a diverse and competent workforce, we propose a decentralized digital resume-based job applicant screening and appraisal framework via BIM, AI, and Blockchain. First, we develop a blockchain job applicant data model that distinguishes between personal privacy data and work-related data for record and storage. A permissioned Blockchain is then designed to facilitate partial transparency for potential employers while ensuring the confidentiality of applicants’ sensitive information. Specifically, for personal privacy data, sensitive attributes (gender, race, etc.) are safeguarded via encryption, and data (address, etc.) about company preferences (the desired distance range from the company, etc.) is also secured while allowing for employer verification via Zero-Knowledge Proofs and smart contracts for information protection. Utilizing time-stamped authentication, applicants’ work history (reference network-based and performance-based information) remains immutable and is securely accessible by potential employers. Based on the validated applicant data and diverse company requirements, the digital resume is generated and customized for each position through smart contracts. For validation, a prototype system is developed with the data from LinkedIn. The results show its feasibility for trusted, fair, secure, and effective construction recruitment.

Pfarelo Raliphada, Micheal O. Olusanya, Seun. Olukanmi

This study examines blockchain algorithms’ performance in a fog computing environments using data from the Bitcoin blockchain. As the demand for secure, low-latency solutions in IoT increases, blockchain offers integrity and decentralization, while fog computing ensures responsiveness. Key performance metrics like throughput, latency, and energy consumption were analyzed using statistical methods. The findings indicate significant variability in throughput and latency, along with high energy demands from traditional consensus mechanisms such as Proof of Work (PoW), making it unsuitable for fog environments. The study recommends adopting lightweight consensus protocols like Proof of Authority (PoA) and Delegated Proof of Stake (DPoS) to improve blockchain performance in edge environments.

Jian Ping, Shiting Kong, Zheng Yan, Xiaoyuan Xu · 5 authors

No abstract is available for this record.

Dr. Laila O. Karim

Rollups are central to blockchain scalability, but their governance is still evolving. Existing voting models risk capture by large stakeholders or inactive delegates. This paper introduces RepRoll, a governance model that uses delegated reputation scores backed by verifiable activity logs. Reputation grows through provable contributions: fraud-proof submissions, code audits, uptime guarantees, and community moderation. These contributions are recorded through a decentralized attestation layer similar to optimistic verification. Votes in protocol upgrades weigh both token stake and reputation, reducing plutocratic influence. A simulation of 10,000 participants demonstrates that RepRoll improves proposal quality and reduces governance attacks. We deploy a prototype on an Ethereum Layer-2 testnet, showing low on-chain overhead. The paper discusses vulnerabilities such as collusion, reputation laundering, and sybil amplification, and proposes cryptographic mitigations.

Matteo Bonato, Rıza Demirer, Rangan Gupta, Abeeb Olaniran

This paper explores the role of mining activity, proxied by growth rates of electricity consumption and cost of mining, as a driver of pricing inefficiencies in Bitcoin. Utilizing alternative measures of crash risk proxied by the realized negative coefficient of skewness and realized down-to-up volatility, derived from 5-minute intraday Bitcoin data, causality tests, along with sign analysis, captured by the estimates of partial average derivatives, provide evidence that mining activity can, in general, predict an increase in the entire conditional distribution of crash risk, with the strongest impact associated over the normal (median) to moderately high (upper quantiles) levels of risk. Despite the emergence of cryptocurrencies in international transactions and as an investment vehicle, our results suggest that decentralized mining process can contribute to inefficiencies in the pricing of Bitcoin, putting further doubt into the role of these assets as a medium of exchange, alternative to conventional assets.

A. Rageshnithin, C. Vanmathi, R. Mangayarkarasi

In the contemporary global job market, the secure and efficient verification of a candidate’s academic qualifications presents a significant challenge, particularly across international boundaries. Conventional techniques frequently necessitate physical documents or PDF scans, rendering them inefficient, susceptible to falsification, and hazardous about privacy. This study presents a contemporary, scalable framework that integrates Zero-Knowledge Proofs (ZKPs), blockchain technology, and decentralized storage (IPFS) to establish a secure, privacy-oriented method for candidate verification. In this proposed system, candidates submit their academic documents, which are digitally signed by the issuing universities using cryptographic methods. The signed files are preserved on IPFS, guaranteeing their integrity and accessibility. The hash of each document is then stored on a blockchain, either Ethereum or Polygon, offering a public and immutable reference. Zero-Knowledge Proofs enable candidates to validate the legitimacy of their credentials while safeguarding sensitive information. Human Resources teams can authenticate these documents in real time, validating their integrity against the blockchain hash while preserving the candidate’s confidentiality. The evaluation results demonstrate that Ethereum offers robust decentralization and trust; nevertheless, Polygon proved to be more pragmatic because to its reduced gas price and expedited transaction times, making it suitable for high-volume recruitment. This proposed initiative addresses weaknesses in digital recruitment by guaranteeing trust, privacy, and automated credential verification procedure. It provides a customized approach for present recruitment requirements, particularly for organizations engaged in cross-border hiring, where security, scalability and protection of candidate information are paramount.

Paula Ungureanu

Blockchain is one of the most consequential innovations since the world wide web. Although blockchain is argued to remove, displace, or redistribute expertise, there is little understanding of the role of expertise in blockchain ecosystems, and more generally the expertise that fuels the development of new technologies by means of open, fluid, and heterogeneous knowledge contributions. An empirical study of the social organization of the Ethereum community, the second largest blockchain ecosystem after Bitcoin, reveals the contrasting tensions involved in setting up a system of decentralized expertise. The alternate community mantras “rough consensus, running code” and “wide consensus, better code?” suggest that the Ethereum community enacts expertise centralization and decentralization practices simultaneously to create a fragile balance between individualized accountabilities and a generalized sense of diffused participation. These practices unfold along a continuum of routine operations punctuated by critical events and are both essential for navigating the uncertainties of decentralized organizations. The study contributes to research on new forms of expertise occasioned by emerging technologies, and in particular to our understanding of blockchain expertise. The study’s relational perspective on expertise adds to research on the dynamics of knowledge de/centralization in online communities.

Abdullah Ayub Khan, Abdulmajeed Alsufyani, Nawal Alsufyani, Mohamad Afendee Mohamed · 5 authors

Environmental deterioration can cause major issues like air pollution, water scarcity, land degradation, and socioeconomic disruptions in heavily mined places like Sindh, Pakistan's Thar coalfields. To overcome these obstacles, a novel strategy using contemporary monitoring and prediction technology is required. This study presents a novel framework for tracking and reducing the environmental effects of mining in poor nations by combining data from blockchain technology, Temporal Convolutional Networks (TCNs), and remote sensing. To ensure stakeholder confidence and accountability, the proposed architecture recodes environmental data using Blockchain Distributed Ledger Technology (BDLT) in a secure, transparent, immutable, and secure manner. The primary potential is to periodically monitor key metrics like vegetation loss, water depletion, and air quality using the Remote Sensing (RS) approach. However, by examining temporal data, TCNs are able to predict trends in environmental degradation and take pre-emptive steps to prevent damage. With a prediction performance of up to 97.3%, metrics such as the Normalised Difference Vegetation Index (NDVI), Air Quality Index (AQI), and water table depth are assessed with great accuracy. In addition to offering politicians and regulators useful information, the proposed architecture uses chaincode to guarantee adherence to environmental regulations. Furthermore, this paper offers a scalable and adaptable solution to environmental limitations in resource-rich places. It supports international sustainability objectives and sets the standard for more ethical mining methods in underdeveloping countries.

Kamalika Bhowal, Srijit Mondal, Abir Chattopadhyay, Kousik Dasgupta

In our Society public opinion surveys are necessary for understanding societal viewpoints, but the conventional polling platforms are at a high risk of forgery, data breaches and manipulation. To ensure the reliability and integrity of polling outcomes, there is a growing need for secure and transparent mechanisms [1]. This paper presents ElectraGuard, a blockchain-based online polling platform designed to deliver trustworthy, tamper-resistant, and user-friendly opinion polling. Built on the Ethereum blockchain using smart contracts, ElectraGuard ensures decentralised execution, voter anonymity, and one-response-per-participant integrity [2]. The system leverages cryptographic hashing and distributed ledger technology to record each submission immutably, preventing result falsification or post-hoc modification. Supporting multiple categories of institutional and organizational polls, the platform features a web-based interface that enables secure participation and real-time result visualization. Testing shows that ElectraGuard greatly improves the security, ability to check results, and trustworthiness of online polling, providing a strong base for clear and checkable digital surveys.

Rabimba Karanjai, Yang Lu, Lei Xu, Weidong Larry Shi

Generative AI and modern Machine Learning are rapidly transforming industries, creating new tools and use cases. As these models grow, there is an increasing need for reliable Machine Learning Operations (MLOps) to manage and deploy them. At the same time, demand is rising for smarter smart contracts and more complex on-chain computation. Our paper presents a new framework that brings GenAI inference directly onto the blockchain. Using the Cosmos SDK, Ethermint, and the ONNX runtime, we enable AI models to run on-chain across multiple blockchain nodes. We evaluate the system’s feasibility, performance, and portability, showing that it can support different AI engines and model types. By enabling GenAI inference on-chain, our approach expands what smart contracts can do, especially for data-driven DeFi, and opens the door to new applications in the future.

Sue-Chen Hsueh, P. C. Yeh

No abstract is available for this record.

Longji Li, Yue Zheng, Songlei Wang, Zhongyun Hua · 6 authors

Graph learning has garnered increasing attention in recent years, which aims to train machine learning models over graph data to support various graph analytic tasks. Coming with the popularity of graph learning are critical privacy concerns regarding the information-rich graphs in many application domains (e.g., finance, social networks, and healthcare). There is thus an urgent call for privacy-preserving graph learning. In this paper, we target an emerging decentralized graph scenario, where a graph is fully decentralized among a set of nodes in such a way that each node only has a limited local view about the global graph. We propose PDGL, a new system framework that can effectively support privacy-assured model training over a decentralized graph, with privacy protection for the links among the nodes as well as the nodes’ private feature data and labels. In contrast to PDGL, prior work does not provide protection for the nodes’ links, feature data, and labels simultaneously. Extensive experiments demonstrate that while providing strong privacy protection for decentralized graph data, PDGL can achieve model utility comparable to the baseline setting of centralized graph learning.