Blockchain Papers

Tianqi Wu, Guyue Li, Jiaheng Wang, Bin Xiao · 5 authors

No abstract is available for this record.

Yi Qin, Xiwen Liu, Xin Li, Yongfang Mao

No abstract is available for this record.

Yusuff Taofeek Adeshina

As the U.S. healthcare system shifts toward value-based care, there is a growing need for patient-centered technologies that ensure data ownership, interoperability, and trust.This paper proposes a novel framework integrating neuro-symbolic artificial intelligence (AI) and zero-knowledge (ZK) blockchain to enable a secure, scalable, and ethically grounded digital-twin marketplace for patient data.The goal is to empower individuals to own and control their health digital twins-comprehensive, dynamic, AI-driven models representing real-time physiological, behavioral, and clinical states-while facilitating precision care and research collaborations.At a macro level, the framework leverages neuro-symbolic AI to enhance digital twin reasoning, enabling explainable predictions and treatment simulations across diverse datasets.This is paired with ZK-proof blockchain infrastructure to ensure privacy-preserving authentication, decentralized governance, and monetization of patient data without revealing sensitive health information.The integration addresses key challenges in trust, transparency, and consent in patient-provider and patient-researcher relationships.Zooming into operational layers, the paper outlines a decentralized application (dApp) architecture that supports smart contracts for patient-informed data sharing, automated payer-provider interactions, and regulatory compliance tracking.It also highlights how incentives within the marketplace can align with care quality metrics, promote social determinants of health inclusion, and advance equitable data access in underrepresented populations.Case scenarios in chronic disease management and clinical trials illustrate the feasibility of this patient-owned digital-twin ecosystem.Ethical considerations, including algorithmic fairness, data sovereignty, and digital consent protocols, are also critically examined.By combining symbolic logic, neural learning, and cryptographic assurance, this framework sets the foundation for a secure and equitable next-generation health economy.

Le Sun, Shunqi Liu, Zhiguo Qu, Yanchun Zhang

No abstract is available for this record.

Ardi Kolah

No abstract is available for this record.

Khandakar Md Shafin, Saha Reno

Abstract Finding a way to solve the trilemma, which requires striking a balance between scalability, security, and decentralization, is a persistent problem in the field of blockchain technology. In order to overcome this trilemma, this study presents a novel blockchain architecture that combines cutting-edge cryptography techniques, creative security protocols, and flexible decentralization mechanisms. Our framework is a new standard for secure, scalable, and decentralized blockchain ecosystems. It utilizes well-known techniques like zero knowledge proof (zk-SNARK), Schnorr VRF, elliptic curve cryptography (ECC), and in addition to innovative approaches for anomaly detection, incentive alignment, and stake distribution. The suggested system outperforms elite consensuses by obtaining 1600+ TPS, guaranteeing strong security against all known blockchain attacks without sacrificing scalability, and obtaining a strong decentralization score of 7.181, which, when compared to other blockchain systems in benchmark analysis, shows strong decentralization.

Krzysztof Gogol, Szczepan Gurgul, Faizan Nehal Siddiqui, David Branes · 5 authors

Ethereum's scalability limitations pose significant challenges for the adoption of decentralized applications (dApps). Zero-Knowledge Rollups (ZK Rollups) present a promising solution, bundling transactions off-chain and submitting validity proofs on-chain to enhance throughput and efficiency. In this work, we examine the technical underpinnings of ZK Rollups and stress test their performance in real-world applications in decentralized finance (DeFi). We set up a proof-of-concept (PoC) consisting of ZK rollup and decentralized exchange, and implement load balancer generating token swaps. Our results show that the rollup can process up to 71 swap transactions per second, compared to 12 general transaction by Ethereum. We further analyze transaction finality trade-offs with related security concerns, and discuss the future directions for integrating ZK Rollups into Ethereum's broader ecosystem.

Xiaolan Shang

As blockchain technology drives the global expansion of the digital currency market, the widespread adoption of high-frequency trading and cross-market arbitrage strategies poses dual challenges to traditional regulatory measures in terms of timeliness and accuracy. This study constructs a hybrid neural network model that integrates supervised and unsupervised learning to explore multi-dimensional feature fusion paths between on-chain data from blockchain and secondary market price data. Based on dynamic game theory, an intelligent regulatory sandbox system is designed, incorporating on-chain address reputation scoring mechanisms and liquidity smart contract circuit breakers to achieve real-time warnings and responses to market manipulation behaviors. Furthermore,a distributed regulatory framework built on zero-knowledge proof technology is proposed, providing a feasible solution for establishing a penetrating regulatory system while ensuring transaction privacy.

Oyegoke Oyebode

The rapid growth of artificial intelligence (AI) in decentralized systems such as healthcare, financial networks, and autonomous transportation has underscored the critical need for interpretability, fairness, and verifiable trust in decision-making. Traditional federated learning frameworks, while addressing data privacy and scalability, often suffer from bias propagation, opaque model behaviors, and limited mechanisms for ensuring accountability. This article introduces Chain-of-Trust AI, a novel paradigm that integrates zero-knowledge proofs (ZKPs), federated reinforcement learning (FRL), and generative learning models to create an interpretable, bias-free, and verifiable decision-making framework for complex distributed environments. The proposed framework leverages FRL to enable adaptive coordination across heterogeneous agents while maintaining local data sovereignty. Generative learning models, such as variational autoencoders, provide transparent causal representations that support bias detection and enhance interpretability of reinforcement-driven policies. ZKPs are embedded as cryptographic guarantees to verify model updates and decision outcomes without exposing sensitive information, thus ensuring compliance, trust, and transparency across decentralized networks. Methodologically, the framework is evaluated through MATLAB-based multi-agent simulations, benchmarking performance in terms of interpretability, fairness indices, convergence stability, and verification overhead. Theoretical analyses confirm convergence under heterogeneous reward structures, cryptographic soundness of proofs, and bias reduction capabilities through generative regularization. Case studies in decentralized healthcare diagnostics, financial fraud detection, and autonomous vehicular coordination highlight the practical scalability and robustness of Chain-of-Trust AI. By uniting reinforcement learning, generative interpretability, and zero-knowledge verification, this work pioneers a secure, auditable, and ethically aligned AI architecture for decentralized complex systems, advancing both technical rigor and governance in distributed intelligence.

Steven Boyer, Cameron McA. Gordon, Ying Hu

Abstract In this article, we show that all cyclic branched covers of a Seifert link have left‐orderable fundamental groups, and therefore admit co‐oriented taut foliations and are not ‐spaces, if and only if it is not an link up to orientation. This leads to a proof of the link conjecture for Seifert links. When is an link up to orientation, we determine which of its canonical ‐fold cyclic branched covers have nonleft‐orderable fundamental groups. In addition, we give a topological proof of Ishikawa's classification of strongly quasi‐positive Seifert links and we determine the Seifert links that are definite, resp., have genus zero, resp. have genus equal to its smooth 4‐ball genus, among others. In the last section, we provide a comprehensive survey of the current knowledge and results concerning the link conjecture.

K. N. Unnikrishnan, Victer Paul Victer Paul P

Distributed trust systems have been transformed by blockchain technology; however, scalability and privacy preservation remain major obstacles. Blockchain-based ridesharing platforms, which provide decentralization, privacy, and enhanced user control inside the system, have been offered as a solution to these problems. Blockchain-based ridesharing services have scaling problems in spite of these benefits. These systems' performance declines with an increase in users, which restricts their usefulness in high-volume marketplaces. To overcome these constraints, this study investigates how blockchain topologies can use zero-knowledge proof (ZKP) approaches. We provide a thorough examination of the current ZKP implementations in blockchain systems, such as zk-Rollups and Bulletproofs, assessing their theoretical underpinnings, real-world uses, and performance indicators. Our study shows that although ZKP integration can increase throughput through rollup technologies and greatly improve privacy guarantees, computational overhead and implementation difficulties continue to be obstacles to broad adoption. Provide a framework for ZKP integration that is tailored to balance privacy, scalability, and usability. This could move blockchain technology closer to more useful real-world applications.

Juan Alamrio Berrios Moya, John Ayoade, Md. Ashraf Uddin

Academic credential fraud presents a significant challenge to the global academic and labor markets, undermining the credibility of legitimate qualifications. In this paper, we introduce ZKBAR-V, a Zero-Knowledge Proof-Enabled Blockchain-Based Academic Record Verification System. This system is designed to provide a privacy-preserving, immutable, and secure framework for managing academic credentials. The proposed system leverages zkEVM smart contracts on a blockchain-based infrastructure that enables credential verification without exposing underlying data. The approach integrates Decentralized Identifiers (DIDs) to standardize identity management while eliminating reliance on centralized authorities. We have used dual-blockchain, which separates public and private information, which can enhance both efficiency and privacy. In addition, this approach employs the Interplanetary File System (IPFS) for decentralized and secure document storage. ZKBAR-V is designed as an open-source, interoperable solution with a standardized Application Programming Interface (API) for seamless integration. We implemented the system and conducted comprehensive testing, which demonstrates its capability to manage transactions securely, maintain privacy, and reduce costs compared to traditional Ethereum mainnet-based solutions. By combining advanced blockchain technologies, decentralized storage, and globally unique identifiers, ZKBAR-V offers a scalable, adaptable, and robust solution for academic credential management. This strategy can significantly enhance credential integrity, promote global student mobility, and provide institutions worldwide with a trustworthy and efficient verification system.

Ziyin Zhang, Jiahao Xu, Zhiwei He, Tian Liang · 13 authors

Theorem proving serves as a major testbed for evaluating complex reasoning abilities in large language models (LLMs). However, traditional automated theorem proving (ATP) approaches rely heavily on formal proof systems that poorly align with LLMs' strength derived from informal, natural language knowledge acquired during pre-training. In this work, we propose DeepTheorem, a comprehensive informal theorem-proving framework exploiting natural language to enhance LLM mathematical reasoning. DeepTheorem includes a large-scale benchmark dataset consisting of 121K high-quality IMO-level informal theorems and proofs spanning diverse mathematical domains, rigorously annotated for correctness, difficulty, and topic categories, accompanied by systematically constructed verifiable theorem variants. We devise a novel reinforcement learning strategy (RL-Zero) explicitly tailored to informal theorem proving, leveraging the verified theorem variants to incentivize robust mathematical inference. Additionally, we propose comprehensive outcome and process evaluation metrics examining proof correctness and the quality of reasoning steps. Extensive experimental analyses demonstrate DeepTheorem significantly improves LLM theorem-proving performance compared to existing datasets and supervised fine-tuning protocols, achieving state-of-the-art accuracy and reasoning quality. Our findings highlight DeepTheorem's potential to fundamentally advance automated informal theorem proving and mathematical exploration.

Yitao Zhao, Xinglong Liu, Yiming Zhang, Jiahao Li

Aiming at the problems of data security and privacy protection in the traditional power metering data sharing mode, this paper puts forward an architecture design of power metering data sharing platform based on blockchain, and deeply analyzes its multi-party security. The platform adopts hierarchical architecture, including data layer, network layer, consensus layer, contract layer and application layer. The data can not be tampered with and can be traced through distributed ledger technology, and the improved DPoS consensus algorithm and intelligent contract technology are used to ensure data consistency and automatic processing. In terms of security, differential privacy, zero-knowledge proof and improved PBFT fault-tolerant model are adopted to effectively resist data tampering, unauthorized access and potential attacks. Through case analysis, the results show that the platform has obvious advantages in data integrity protection, access control, privacy protection and inter-agency collaboration efficiency improvement, and shows good adaptability in resource consumption. The research shows that blockchain technology provides a safe, efficient and reliable solution for power metering data sharing.

Priyanshu Singh, Princi, Murari Kumar Singh, Santosh Kumar Verma

Scalability remains a major challenge in blockchain technology, particularly for Layer-1 networks like Ethereum, where high transaction volumes cause congestion and high gas fees. Zero-Knowledge (ZK) Rollups have emerged as scalable Layer-2 solutions, offering enhanced security and lower transaction costs. However, existing zk-Rollups such as zkSync Era, dYdX, and StarkNet each have their advantages and limitations in terms of efficiency, cost, and decentralization. This paper proposes zkFusion, a hybrid zk-Rollup protocol that combines the most effective features of these rollups to achieve faster transaction rates, improved cost-effectiveness, and enhanced security. zkFusion employs a flexible proof mechanism, optimized transaction batching, and a modular data throughput method, allowing users to balance between cost and security. Additionally, it integrates a decentralized sequencer to enhance network reliability and mitigate centralization risks. Comparative evaluation with existing zk-Rollups demonstrates that zkFusion significantly reduces transaction fees, improves throughput, and maintains high security assurances. By blending the innovations of zkSync Era, dYdX, and StarkNet, zkFusion aims to set a new benchmark for scalability, efficiency, and cost reduction in Layer-2 blockchain solutions.

Stephan Rabanser, Ali Shahin Shamsabadi, Olive Franzese, Xiao Wang · 6 authors

Cautious predictions -- where a machine learning model abstains when uncertain -- are crucial for limiting harmful errors in safety-critical applications. In this work, we identify a novel threat: a dishonest institution can exploit these mechanisms to discriminate or unjustly deny services under the guise of uncertainty. We demonstrate the practicality of this threat by introducing an uncertainty-inducing attack called Mirage, which deliberately reduces confidence in targeted input regions, thereby covertly disadvantaging specific individuals. At the same time, Mirage maintains high predictive performance across all data points. To counter this threat, we propose Confidential Guardian, a framework that analyzes calibration metrics on a reference dataset to detect artificially suppressed confidence. Additionally, it employs zero-knowledge proofs of verified inference to ensure that reported confidence scores genuinely originate from the deployed model. This prevents the provider from fabricating arbitrary model confidence values while protecting the model's proprietary details. Our results confirm that Confidential Guardian effectively prevents the misuse of cautious predictions, providing verifiable assurances that abstention reflects genuine model uncertainty rather than malicious intent.

T Gomathi, S. Maflin Shaby, Saroo Raj R B, Prathap Kumar K

Body Area Network (BAN) systems ensure that physically integrated wearable and implantable biomedical devices are networked to make health monitoring possible. However, one of the major difficult issues that researchers and practicing health professionals have continued to face is protecting identified sensitive health information while at the same time addressing the consumer's right to privacy. ZHIs can offer significant benefits to assist BAN systems improve data security and privacy by enabling one party to prove the possession of certain information without revealing it to another party. This study focuses on the BAN into which ZKPs are incorporated to enhance the security of authentication, access, and data sharing among the stakeholders that include the healthcare givers and patients. Through the use of ZKPs, the proposed approach ensures that only the right people can prove the authenticity of the health data without revealing the data hence reducing on the risk of data leakage and other related issues. COVERY is specifically designed to have a low computational overhead for BAN devices by utilizing the ZKP technique. In this case, following a discussion of the proposed scheme, the simulation and real-world mode of the scheme are carried out and analyzed to determine its effectiveness and feasibility. The BAN systems enhanced through the integration of ZKP are found to enhance the data privacy of a network, decrease the attack angles and also ensure faster transfer of data securely in a healthcare network. This paper discusses how ZKPs can be adopted as a revolutionary solution for achieving privacy-preserving solutions in healthcare.

Aditi Rai, Vijay Kumar Yadav

The major challenge in the existing communication system is maintaining the user's privacy while ensuring the pro- cess of verification and authentication. The conventional methods either jeopardize with user's privacy by linking the data or message to the source or fail to prevent false submissions because of weak authentication mechanisms. To address these issues, this paper proposes a Zero-Knowledge Proofs-based Quantum- resistant Anonymous and Authenticated Feedback System that optimizes Zero-Knowledge Succinct Non-Interactive Argument of Knowledge, shortly termed as zk-SNARKs, to enable secure, anonymous, and verifiable feedback submissions. The method presented in this research achieves strong authentication without sacrificing user privacy, which was not possible with traditional techniques like digital signatures, public-key infrastructure, and others. The system is resistant to impersonation and Sybil attacks because it uses zk-SNARKs to enable users to authenticate their permission to send feedback without disclosing their identity. Furthermore, the suggested framework is made to be postquantum secure, guaranteeing long-term resilience against sophisticated quantum attackers, since quantum computing poses a danger to traditional cryptographic techniques like RSA and ECC. Security, effectiveness, and practical viability of the system are assessed, based on which it is concluded that zk-SNARKs are a reliable and scalable basis for privacy-preserving feedback mechanisms and in various other applications like online dis- cussion forums, educational assessments. The study highlights how well zk-SNARKs succeeds in making a privacypreserving authentication system and mitigating the risk of quantum attacks.

R Sheeba, Jay Prakash Mahto, Syed Sabith Ansari, Zian Rajeshkumar Surani · 6 authors

The model presented in this work represents a paradigm shift that sets a completely novel standard for data distributed validation in ethical AI training. Our new paradigm integrates fault-tolerant Byzantine consensus along with zero-knowledge proofs for secured and provable auditing of data within decentralized AI systems. The framework uses a two-layer blockchain design that separates metadata anchoring from validation logs, allowing it to achieve an instantaneous compliance check time of less than one second while maintaining privacy compliance to GDPR. Key innovations comprise a sharded Merkle-Patricia Trie kind for dynamic data lineage chains, the application of differential privacy and federated learning with bias-neutralizing validation oracles, as well as the design of incentive engineering under a non-Markovian reward system for multiple agents. The results of experimentation prove that, under adversarial conditions, the detection of anomalies is 40% faster than centralized alternatives, while maintaining an integrity verification of the audit trail at 99.99%. The collaboration between AI explainability matrices and post-quantum secure voting mechanisms in this work set innovative standards for decentralized ethical oversight of mission-critical operations, thus transforming the trust dynamics among model developers, data subjects, and auditors.

Mihajlo Karadžić, Dušan Mačkić, Sandra Rakočević, Marija Antić

Traditional STB management systems rely on TR-069, which faces challenges in scalability, security, and efficiency when dealing with modern IoT-based deployments. We compare WISE with TR-069 in terms of latency, scalability, and security, incorporating Zero-Knowledge Proof (ZKP) authentication to strengthen access control. Our evaluation demonstrates that WISE significantly reduces management latency and scales more efficiently compared to TR-069 while ensuring a more secure authentication process.

Mark Aronoff, Robert M. Townsend

We construct an empirically founded model of a repo trade intermediated by two broker-dealers and prove multiple equilibrium and the existence of equilibrium at the joint profit maximizing volume of trade. We then present a smart contract that resolves multiple equilibrium by requiring each broker-dealer to report its client schedule and its minimum hurdle spread, and implementing a selection rule that filters out hurdle-infeasible outcomes. Whenever there exists an equilibrium that exceeds both hurdle spreads, the protocol selects the joint profit maximizing feasible trade and thereby avoids a collapse to no trade. The smart contract is a machine executed algorithm which eliminates the need for trust. Hardware and cryptography are used to prevent leakage of broker-dealer client trade schedules, and to enable privacy-protected auditing with zero-knowledge proofs of the integrity of computations. The outcome can be implemented by a myopic strategy where a broker-dealer truthfully reports its own variables without anticipating its counterparty's reports. This minimizes cognitive and computational complexity, thereby making our smart contract suitable for real-world deployment.

Caixiang Fan, Amirhossein Sohrabbeig, Petr Musı́lek

Blockchain-based peer-to-peer energy trading enables individuals to directly share renewable energy using Internet of Things technologies. However, it faces significant challenges related to privacy, scalability, and the integration of advanced artificial intelligence. To address these issues, this article proposes zkPET, a secure and intelligent peer-to-peer energy trading framework. zkPET integrates machine learning and blockchain with advanced cryptographic techniques of zero-knowledge machine learning to protect user data while enabling intelligent decision making. In the zkPET framework, the computationally intensive operations of various machine learning models are executed off-chain, and only succinct cryptographic proofs of these computations are uploaded to the blockchain for verification and recording. In addition, a time-series clustering approach is incorporated into federated learning to enhance both inference accuracy and the efficiency of proof generation. Experimental validation using the zero-knowledge proof tool EZKL and a real-world electricity dataset demonstrates the feasibility and effectiveness of zkPET. The results underscore its potential to significantly improve privacy, scalability, and computational efficiency in decentralized energy trading, contributing to the advancement of secure and intelligent energy markets.

Abhijit Talluri

Federated Learning (FL) has emerged as a critical paradigm for enabling privacy-preserving machine learning, particularly in regulated sectors such as finance and healthcare. However, standard FL strategies often encounter significant operational challenges related to fault tolerance, system resilience against concurrent client and server failures, and the provision of robust, verifiable privacy guarantees essential for handling sensitive data. These deficiencies can lead to training disruptions, data loss, compromised model integrity, and non-compliance with data protection regulations (e.g., GDPR, CCPA). This paper introduces Differentially Private Resilient Temporal Federated Learning (DP-RTFL), an advanced FL framework designed to ensure training continuity, precise state recovery, and strong data privacy. DP-RTFL integrates local Differential Privacy (LDP) at the client level with resilient temporal state management and integrity verification mechanisms, such as hash-based commitments (referred to as Zero-Knowledge Integrity Proofs or ZKIPs in this context). The framework is particularly suited for critical applications like credit risk assessment using sensitive financial data, aiming to be operationally robust, auditable, and scalable for enterprise AI deployments. The implementation of the DP-RTFL framework is available as open-source.

Hasina Andriambelo, Naghmeh Moradpoor, Λέανδρος Μαγλαράς

Decentralized and Connected Autonomous Vehicle (CAV) networks offer promising advances in safety, efficiency, and real-time decision-making. However, they face significant challenges in authentication, privacy, and scalability—especially in the face of quantum adversaries. This paper proposes a novel post-quantum secure framework that integrates lattice-based Zero-Knowledge Proofs (ZKPs), optimized Binius proofs, and Multi-Layer Compressed Counting Bloom Filters (ML-CCBF) to enable privacy-preserving authentication and model verification in decentralized CAV environments. Our lattice-based ZKP scheme achieves cryptographic commitments in under 5μs, while Binius proofs verify model integrity in less than 0.17 seconds per update. ML-CCBF ensures scalable membership filtering with 0% false positives across 1000 nodes. Experimental results confirm 100% ZKP soundness, strong resilience against simulated quantum and adaptive attacks, and stable latency under increasing network load. These findings demonstrate that our framework delivers quantum-resilient security, real-time efficiency, and robust scalability, offering a viable solution for trustworthy decentralized intelligence in next-generation vehicular systems.