Blockchain Papers

Kai Li, Yanyu Chen, Zhangjie Fu

No abstract is available for this record.

Amandeep Singh Bhatia, Sabre Kais

Federated Learning (FL) has become increasingly popular across different sectors, offering a way for clients to work together to train a global model without sharing sensitive data. It involves multiple rounds of communication between the global model and participating clients, which introduces several challenges like high communication costs, heterogeneous client data, prolonged processing times, and increased vulnerability to privacy threats. In recent years, the convergence of federated learning and parameterized quantum circuits has sparked significant research interest, with promising implications for fields such as healthcare and finance. By enabling decentralized training of quantum models, it allows clients or institutions to collaboratively enhance model performance and outcomes while preserving data privacy. Recognizing that Fisher information can quantify the amount of information that a quantum state carries under parameter changes, thereby providing insight into its geometric and statistical properties. We intend to leverage this property to address the aforementioned challenges. In this work, we propose a Quantum Federated Learning (QFL) algorithm that makes use of the Fisher information computed on local client models, with data distributed across heterogeneous partitions. This approach identifies the critical parameters that significantly influence the quantum model's performance, ensuring they are preserved during the aggregation process. Our research assessed the effectiveness and feasibility of QFL by comparing its performance against other variants, and exploring the benefits of incorporating Fisher information in QFL settings. Experimental results on ADNI and MNIST datasets demonstrate the effectiveness of our approach in achieving better performance and robustness against the quantum federated averaging method.

Maria Nuțu, Giorgi Akhalaia, Răzvan Bocu, Maksim Iavich

Commitment schemes represent foundational cryptographic primitives enabling secure verification protocols across diverse applications, from blockchain systems to zero-knowledge proofs. This paper presents a systematic survey of vector, polynomial, and functional commitment schemes, analyzing their evolution from classical constructions to post-quantum secure alternatives. We examine the strengths and limitations of RSA-based, Diffie–Hellman, and lattice-based approaches, highlighting the critical shift toward quantum-resistant designs necessitated by emerging computational threats. The survey reveals that while lattice-based schemes (particularly those using the Short Integer Solution problem) offer promising security guarantees, they face practical challenges in proof size and verification efficiency. Functional commitments emerge as a powerful generalization, though their adoption is constrained by computational overhead and setup requirements. Key findings identify persistent gaps in adaptive security, composability, and real-world deployment, while proposed solutions emphasize optimization techniques and hybrid approaches. By synthesizing over 90 research works, this paper provides both a comprehensive reference for cryptographic researchers and a roadmap for future developments in commitment schemes, particularly in addressing the urgent demands of post-quantum cryptography and decentralized systems.

Yinshi Li, Wenqiang Gu

Effective data governance is crucial in modern digital ecosystems, ensuring secure, transparent, and efficient data sharing. Traditional centralized governance models often suffer from trust issues, inefficiencies, and security vulnerabilities. Blockchain technology offers a decentralized and tamper-resistant solution to address these challenges. This paper proposes a blockchain-based data governance architecture that enhances data sharing mechanisms and optimizes smart contract execution. The framework leverages a permissioned blockchain to ensure controlled data access while maintaining data integrity and security. To further improve performance, an optimized smart contract mechanism is introduced using gas-efficient transaction designs and layer-2 scaling solutions. Experimental evaluations demonstrate that the proposed model improves transaction efficiency, reduces computational overhead, and enhances security compared to conventional blockchain-based governance systems. The results highlight the potential of blockchain in establishing a decentralized, efficient, and transparent data governance framework for secure and scalable data exchange.

Bimal Ghimire, Danda B. Rawat, Yuba R. Siwakoti

The increasing complexity of computational problems across many scientific and technological domains often challenges traditional centralized computing resources. As the internet evolves toward Web 3.0, blockchain technology is emerging as a foundational infrastructure for decentralization, transparency, and distributed collaboration. Applications like the metaverse, which demand real-time responsiveness and high computational throughput, further underscore the need for scalable and resilient computing frameworks. In this regard, we propose a novel framework for crowdsourcing computationally intensive tasks using blockchain and smart contracts. Operating atop existing blockchain networks, a Master (or Requester) node defines a computational problem, decomposes it into subtasks, and deploys a smart contract to manage task distribution. Worker nodes perform the computations off-chain using local resources and submit their results via on-chain transactions. The smart contract aggregates these results and finally, the Master validates them and automatically distributes rewards in cryptocurrency. A proof-of-concept simulation using Ganache, Solidity, and off-chain scripts demonstrates the feasibility of this approach, showcasing key features such as task decomposition, off-chain computation, and automated result handling. These findings underscore blockchain’s potential to enable transparent, automated, and scalable coordination of distributed computing tasks.

Lanye Wei, Zhao Liu

The rise of Non-Fungible Tokens (NFTs) and Internet of Things (IoT) devices created new demands for secure data management. To address these needs, we propose LIBLO, a lightweight blockchain-based smart NFT architecture designed for decentralized environments with limited resources. Traditional models mostly depend on heavy computation techniques to ensure the data security. To avoid this, LIBLO introduces a compressed blockchain layer combined with lightweight encryption techniques. This allows secure storage, verification, and controlled access to IoT-generated data without overloading devices. In this architecture, LIBLO acts as a trusted digital framework that securely encapsulates metadata, ownership identity, and access control policies. Each transaction is verified through digital signatures and efficiently recorded on a compressed blockchain ledger. This design ensures privacy, traceability, and integrity and also contributes to energy-efficient implementation in real-time IoT scenarios. Experimental results demonstrate that the suggested LIBLO achieves high encryption strength and strong decryption accuracy of 0.96%, and low error rates of (1.1%). By simplifying cryptographic operations and reducing blockchain complexities, LIBLO presents a practical and adaptable solution for securing digital assets and IoT interactions in smart environments.

Sowjanya Pandruju

With the extensive growth in terms of data and AI adoption across various fields such as finance, healthcare and insurance, data security and privacy have become significant barriers to innovation. This paper provides a privacy-aware framework for distributed AI as a possible solution which is integrated with cloud-native architectures. By leveraging decentralized model training without sharing raw data, this solution offers a compliant and secure framework for deploying machine learning at scale. A scalable and cost-effective system architecture is proposed that aligns with data protection regulations while maintaining high performance and model accuracy. This approach empowers organizations to leverage AI responsibly, unlocking the potential of sensitive data without compromising privacy.

Y. P. Xu

With the widespread application of blockchain technology, the security of private transactions has become a bottleneck restricting further development. This project presents a blockchain privacy transaction optimization model utilizing zero-knowledge proof (ZKP). By extracting data features such as transaction volume, transaction frequency, and counterparty trustworthiness, the model dynamically assigns weights through an entropy-based framework for different transaction scenarios. It also adaptively modifies certificate generation and verification strategies using reinforcement learning to enhance efficiency and security. In terms of experiments, a blockchain simulation environment is constructed, and 100,000 transaction data points are used as samples to compare the DA-ZKP algorithm and the traditional zero-knowledge proof algorithm. The experimental results show that the DA-ZKP algorithm reduces the generation time by 35%, the verification time by 28%, and the memory overhead by 22% on average. At the same time, the algorithm has a privacy protection capability comparable to traditional algorithms and can resist replay and tampering attacks. The optimization model and algorithm proposed in this project can effectively improve the efficiency and security of blockchain privacy transactions and provide a new idea for developing blockchain privacy protection technology.

Kriti Patidar, Swapnil Jain, Mohammad Husain, Mohd Muqeem · 9 authors

As IoT-connected devices, sometimes referred to as the Internet of Things (IoT), continue to proliferate, existing centralized identity management systems struggle in the large scale due to issues with scalability, privacy and security. For these reasons, centralized identity management systems will not meet the requirements of large-scale IoT deployments. In this paper, we suggest a decentralized identity management system to authenticate and authorize IoT devices based on a hybrid blockchain and Zero-Knowledge Proof (ZKP) protocol. The proposed system utilizes decentralized identifiers (DIDs), verifiable credentials (VCs) and a hierarchical web-of-trust structure as part of the identity management process. The identity and credentials can be created and validated in a decentralized manner and locally, using smart contracts and lightweight consensus models such as Proof of Stake (PoS) and Practical Byzantine Fault Tolerance (PBFT). The performance evaluation demonstrated the performance in respect of authentication latency businesses managed to get the latency to 250 ms, throughput reaching to 200 messages per second and energy efficiency improved to 300mW/device. Based on the baseline comparisons including PoW, OAuth and Hash-MAC based systems included, the proposed method is scalably better, provides greater security against DDoS and MITM attacks and used less memory. The proposed method yields a robust, fully decentralized identification system for managing IoT identities without requiring a centralized authority, allowing scalable and secure interactions across distributed networks.

Hongjuan Kang, Bing Guo, Na Shi, Dejun Huang · 5 authors

Abstract In the context of the IoT platform, the smart home represents a quintessential application scenario. Here, device-to-device (D2D) collaboration serves as the core element of its ecosystem, playing a crucial role in implementing diversified automated execution scenarios that are customized to fulfill user requirements. The progressive integration of edge computing and AI technologies has enhanced the collaboration among heterogeneous devices. Nevertheless, the conventional centralized D2D collaboration authorization decision-making supported by a single IoT Hub violates the Principle of Least Privilege (PoLP), which is a foundational design tenet that has been empirically validated as an optimal engineering practice for enhancing system security and reliability in IoT ecosystems. If there is a trade-off of PoLP violations, it fails to meet the users’ Quality of Experience (QoE). To address this issue, we propose D2DA, a distributed authorization decision-making model for smart home D2D collaboration, which constructs a distributed decision-making consensus network suitable for the edge side of smart homes by leveraging distributed ledger technology. D2DA presents a machine learning algorithm with a time complexity of O ( n ). Through this algorithm, consensus nodes can be efficiently and dynamically selected. Furthermore, D2DA ensures the security of the D2D collaboration process via wallets and hash verification. Extensive experiments conducted on a real-world smart home scenario validate that the decision-making latency of D2DA is on par with that of a single IoT Hub mode. The average latency for verifying the correctness of the newly added execution results is only 0.08% of the system time of D2DA, which is negligible.

Kombou Victor, Qi Xia, Hu Xia, Jianbin Gao · 9 authors

Non-fungible token (NFT) markets present a dual analytical challenge: integrating heterogeneous data modalities (high-dimensional visual features and discrete transaction sequences) while preserving privacy for sensitive wallet addresses and trading strategies. Current approaches analyze visual attributes or transaction patterns in isolation, missing critical value drivers from cross-modal interactions. Meanwhile, existing multimodal techniques lack formal privacy guarantees, exposing participants to inference attacks. This article introduces PrivaMod, a privacy-preserving Bayesian framework that addresses these limitations through uncertainty-aware multimodal fusion. Our approach implements precision-weighted Bayesian fusion that dynamically adjusts modality contributions based on quantified uncertainty levels, while integrating Rényi Differential Privacy throughout the pipeline via calibrated noise injection and adaptive gradient clipping. Evaluated on 167,492 CryptoPunk transactions, PrivaMod achieves a market efficiency score of 0.874 and R 2 of 0.912, outperforming existing methods by 13.4% through superior cross-modal integration while maintaining strong privacy guarantees ( \(\varepsilon\) = 0.08, \(\delta\) = 1e-5) with membership inference attack success rates near random guessing (53.4%). The system demonstrates that privacy-preserving techniques can enhance rather than compromise analytical performance, establishing a foundation for responsible market analysis. To ensure reproducibility, we release our code, preprocessed datasets, and model checkpoints with detailed documentation and scripts to replicate all experiments. PrivaMod is available at https://github.com/kvjunior/PrivaMod/blob/main/README.md .

Yang Li, Hanjie Wang, Yuanzheng Li, Jiazheng Li · 5 authors

Wind power data often suffers from missing values due to sensor faults and unstable transmission at edge sites. While federated learning enables privacy-preserving collaboration without sharing raw data, it remains vulnerable to anomalous updates and privacy leakage during parameter exchange. These challenges are amplified in open industrial environments, necessitating zero-trust mechanisms where no participant is inherently trusted. To address these challenges, this work proposes ZTFed-MAS2S, a zero-trust federated learning framework that integrates a multi-head attention-based sequence-to-sequence imputation model. ZTFed integrates verifiable differential privacy with non-interactive zero-knowledge proofs and a confidentiality and integrity verification mechanism to ensure verifiable privacy preservation and secure model parameters transmission. A dynamic trust-aware aggregation mechanism is employed, where trust is propagated over similarity graphs to enhance robustness, and communication overhead is reduced via sparsity- and quantization-based compression. MAS2S captures long-term dependencies in wind power data for accurate imputation. Extensive experiments on real-world wind farm datasets validate the superiority of ZTFed-MAS2S in both federated learning performance and missing data imputation, demonstrating its effectiveness as a secure and efficient solution for practical applications in the energy sector.

Tehrim Yoon, Minyoung Hwang, Eunho Yang

Modern generative models, particularly denoising diffusion probabilistic models (DDPMs), provide high-quality synthetic images, enabling users to generate diverse images and videos that are realistic. However, in a number of situations, edge devices or individual institutions may possess locally collected data that is highly sensitive and should ensure data privacy, such as in the field of healthcare and finance. Under such federated learning (FL) settings, various methods on training generative models have been studied, but most of them assume generative adversarial networks (GANs), and the algorithms are specific to GANs and not other forms of generative models such as DDPM. This paper proposes a new algorithm for training DDPMs under federated learning settings, VQ-FedDiff, which provides a personalized algorithm for training diffusion models that can generate higher-quality images FID while still keeping risk of breaching sensitive information as low as locally-trained secure models. We demonstrate that VQ-FedDiff shows state-of-the-art performance on existing federated learning of diffusion models in both IID and non-IID settings, and in benchmark photorealistic and medical image datasets. Our results show that diffusion models can efficiently learn with decentralized, sensitive data, generating high-quality images while preserving data privacy.

Righa Tandon, Neeraj Sharma

No abstract is available for this record.

Harsh Yadav, Varun Shukla, Hamdan Zaman khan, Anshul Kumar Mandal

The currently existing Aadhaar data sharing system has a dearth of granular user control enabling other third party entities including banks, telecom service providers, and government agencies access to the user's data without explicit, time bound and purpose specific consent. This incites all manner of privacy, transparency, and data misuse concerns. This paper then proposes a Blockchain Based Consent as a Service (CaaS) Framework to guarantee such real time, secure and auditable access of Aadhaar linked data in the hands of users via authorized smart contracts. In the proposed model, the smart contract encapsulates each data access request includes purpose, requesting entity, the type of data, and the duration of access. A secure app or web interface will notify user or browse regarding consent requests, and allow him to accept or refuse request by using Aadhaar Virtual ID and OTP or digital signatures. Once it is approved, the access is granted to consumers on a temporary basis with full logging on a blockchain ledger that is immutable and open for all to see by users and regulators. It is compliant with the Digital Personal Data Protection (DPDP) Act 2023 and utilizes off chain storage of encrypted data. In addition to allowing users to possess granular control over their digital identity, the framework also lays the groundwork for Zero Knowledge Proof (ZKP) based functionalities and AI based anomaly detection to proactively protect users' privacy in the future.

Satya Manesh Veerapaneni, Prateek Sharma, Siva Sankar Das, Jayakanth Tivari · 5 authors

The fact that data generated in distributed sources grows exponentially poses great hurdles on the centralized machine learning workflow, especially regarding data privacy, latency and bandwidth overhead. Federated Feature Stores (FFS)Constitute a new paradigm that perpetrates real-time learning in which data is not aggregated. In this paper, one such architecture is provided in which feature engineering, storage and access, are decentralized and co-located with data sources taking advantage of the principles of federated learning. To satisfy the need to maintain data locality but guarantee consistency, low latency inference and privacy regulations, FFS uses a combination of on-device feature computation combined with updating global models. We are working on federated feature synchronization, version control and optimal caching for heterogeneous environments. The experimental analysis over edge clusters and cloud back ends reveals significant bumps in the end-to-end training throughput, inference latency, and privacy preservation over conventional centralized feature pipelines. The given FFS framework provides a scalable, privacy-sensitive, and efficient replacement of older data engineering pipes in the fields of healthcare, finance and the Internet of Things.

M Rufina, V. Anantha Krishnan, P Gokul, Yanamala Akshaya · 5 authors

Conventional land registration systems are often slow, opaque, and susceptible to fraudulent activities due to centralized control, intermediaries and manual verification. This paper proposes a decentralized framework based on blockchain technology to enhance transparency, security, and efficiency in property registration. The system employs smart contracts to automate ownership transactions and ensures user privacy through cryptographic techniques that verify identity without revealing personal data. Property-related documents are stored securely using a distributed file system, while the network’s validation process operates through an energy-efficient consensus model. An intuitive web interface allows secure interaction and real-time updates through digital wallets. Performance testing confirms the system’s scalability, responsiveness, and resistance to data tampering. This approach offers a promising foundation for modernizing land record systems and can be adapted for adoption by public institutions worldwide.

Trinh Gia Huy, Luong Nguyen Thanh Nhan, Nguyen Tan Cam

No abstract is available for this record.

Ahmed M. Tawfik, Ayman Al-Ahwal, Adly S. Tag Eldien, Hala H. Zayed

Abstract In recent years, blockchain technology has emerged as a promising solution for securing electronic health records (EHRs) while preserving patient privacy. Traditional e-health systems facilitate EHR sharing among healthcare providers but also introduce significant privacy risks, such as unauthorized access and data breaches. Blockchain, when integrated with privacy-preserving techniques, enhances transparency, integrity, and availability in EHR management. Smart contracts further strengthen security by enabling automated authentication and access control. This paper provides a comprehensive survey of blockchain-based access control frameworks in healthcare, categorizing them into permissioned and permissionless approaches. It also explores cryptographic privacy-preserving techniques designed to mitigate privacy risks. Additionally, blockchain platforms and consensus protocols commonly used in these frameworks are analyzed. The methodology follows a structured paper selection process, leading to the final inclusion of 45 research papers focusing on blockchain-based privacy preservation and access control in healthcare. Furthermore, it presents real-world case studies that illustrate the practical implementation of blockchain-based access control in healthcare settings, highlighting their strengths and challenges. Finally, it identifies privacy-related challenges, open research issues, and future directions to guide further research in this evolving domain.

Haewon Byeon, Ankur Chaudhary, Janjhyam Venkata Naga Ramesh, Desidi Narsimha Reddy · 9 authors

The increasing use of federated learning (FL) in healthcare IoT demands rigorous verification to ensure the correctness of remote model training without compromising patient data privacy. However, existing approaches either assume full trust in clients or introduce high computational and communication costs when integrating cryptographic guarantees. In this work, we propose a lightweight, privacy-preserving federated learning framework that integrates zk-SNARK-based verifiable training over a ring topology. Our system ensures that each client’s model update and aggregation step can be independently verified without revealing sensitive data or requiring a central auditor. We design an efficient proof composition strategy (CGro16) tailored for chained convolution operations and commitment schemes optimized for healthcare models. We also introduce a matrix polynomial-based masking mechanism (MatProofs) to support zero-knowledge commitments for convolutional neural networks (CNNs).Experimental results on standard benchmarks (MNIST, CIFAR-100) show up to 47% reduction in proof generation time and 39% lower memory overhead compared to baseline zk-SNARK schemes. The protocol is also benchmarked on edge devices (Jetson Nano, Raspberry Pi), confirming its suitability for remote and wearable healthcare scenarios.

Gayatri M Bhandari, Nitin M Shivale, Shrishail S Patil, Pranav Prajapati · 7 authors

Federated learning is an emerging technology that can revolutionize the training of machine learning models. Federated learning refers to an approach to training a machine learning model in a decentralized and collaborative fashion. A central server distributes the model to client devices, where it is trained locally using the clients’ own data. The client then sends the updated model weights to the server, which aggregates them to update the global model. This paper introduces a federated learning platform designed to enable collaborative training of machine learning models across multiple client devices while preserving data privacy. The platform supports a range of supervised learning algorithms, including convolutional neural networks and decision trees, and is compatible with widely used frameworks such as TensorFlow, PyTorch, and Flower. It offers a user-friendly interface where model developers can upload or deploy their machine learning models to a central server. Clients can then access these models and train them locally using their own data. The platform's modular design ensures flexibility in deployment and efficiency in handling real-world applications. The key features of this application include a model repository, secure API access for client integration, local model training capabilities on user-end devices, and a user-friendly UI. The platform aims to democratize machine learning by enabling distributed model training and deployment, promoting collaboration and efficiency across diverse use cases. The scalable infrastructure supports real-time inference, on-device training, and secure data handling, making it ideal for industries ranging from healthcare to finance and beyond.

Ziyi He, Yuxi Gong, Chuhang Hu

The widespread adoption of big data and AI technologies has accelerated the advancement of intelligent medical diagnostics. However, the sensitivity of medical data poses a dual challenge of privacy leakage and computational inefficiency in cross-institutional collaboration. Traditional federated learning (FL) schemes struggle to balance privacy protection, model accuracy, and communication costs, particularly for real-time processing of high-resolution medical images. To address this, we propose CZ-FLMed, a privacy-preserving FL framework integrating CKKS fully homomorphic encryption (FHE) and zkSNARKs zero-knowledge proofs. The framework employs a customized Convolutional Neural Network (CNN) for medical image training, the CKKS segmented encryption strategy for reducing communication overhead, and the lightweight Groth16 protocol for secure identity verification. This enables efficient encrypted model aggregation and authentication. The experiments results conducted in this paper on Chest X-Ray pneumonia dataset and MNIST handwritten digits demonstrate that CZ-FLMed achieves 83.05 % test accuracy in pneumonia classification. Compared to Paillier encryption, it reduces communication costs by 92.84 % and improves encryption efficiency by 404 times. Thus, the framework balances model accuracy, computational efficiency, and privacy preservation, offering a practical solution for multicenter medical collaboration.

Hao Pham Duc, Khang Vo, Khuong Nguyen - An

No abstract is available for this record.

Sandeep Gajanan Sutar, B M Praveen, Amolkumar N. Jadhav

Cloud computing has transformed data storage and access with flexible and scalable solutions. However, its dependence on third-party services poses significant concerns regarding data privacy and integrity. To tackle these concerns, blockchain technology known for its decentralized structure and tamper resistance offers a promising security enhancement for cloud environments. This paper explores and evaluates various blockchain-based mechanisms for securing cloud data and proposes a hybrid model that integrates blockchain with existing cloud infrastructures. Leveraging consensus protocols and cryptographic hashing, the proposed approach aims to mitigate data breaches, unauthorized access, and tampering. A practical implementation demonstrates the model’s effectiveness in fostering trust, transparency, and reliability in cloud services. Keywords: Block-chain, Data Privacy, Security, Data Integrity, Distributed ledger, Cloud computing