Blockchain Papers

Yuqi Zhao, Zhiming Song, Junrong Song, Hui Tong · 6 authors

No abstract is available for this record.

Mai Shawkat, Ali El-desoky, Zainab H. Ali, Mofreh Salem

Abstract The Industrial Internet of Things (IIoT) applications have been recognized as an advancement of the conventional wireless network that concentrates on incorporating processes and machines specifically for industrial applications. These Industrial applications frequently use centralized machine learning (ML) approaches not only to enhance their functionality but also to evaluate sensor data for a variety of purposes, including digitizing operations in manufacturers, forecasting maintenance requirements in industrial equipment, and detecting anomalies for security monitoring, they may adversely affect overall system performance due to high cost of computing power and privacy concerns, as so much data is stored on a cloud server. Federated Learning (FL) has emerged as a new benchmark for centralized ML methods. It sends models to user devices without transferring private data to third-party or central servers; it is one of the promising solutions to data leakage issues. This work introduces a comprehensive overview of the advancements, challenges, and future directions in FL adoption with edge devices. It covers security threats and mitigation strategies, emphasizing its categories, privacy and concerns, communication overhead obstacles, heterogeneity issues, aggregation techniques, and associated development tools. This review paper delves into FL-related topics, including system platforms, offering a comprehensive overview of best practice systems in real-world FL applications. To ensure security in IIoT applications, reviewing threats and mitigation strategies by integrating FL with state-of-the-art technologies such as blockchain, federated reinforcement learning, and federated meta-learning has been explored. Finally, the recent research is taking place to determine new future directions and opportunities for FL security defense mechanisms has been considered at the end of this review paper.

Murali Krishna Pasupuleti

Abstract: Federated deep learning (FDL) is an emerging paradigm that enables multiple decentralized devices or institutions to collaboratively train a shared model while keeping data localized. This approach preserves privacy, reduces communication overhead, and complies with data governance regulations. In this paper, we explore the implementation and performance of FDL in real-world scenarios such as healthcare, finance, and IoT systems. Utilizing frameworks like TensorFlow Federated, PyTorch, and interpretability tools like SHAP and LIME, we evaluate FDL against centralized deep learning models. We analyze convergence rates, model accuracy, data privacy risk, and computational efficiency. Regression and predictive analyses reveal that FDL can retain over 90% accuracy of centralized models with significantly enhanced data security. Keywords: Federated Learning, Deep Learning, Privacy Preservation, Decentralized Training, TensorFlow Federated, Secure AI, SHAP, LIME, Model Interpretability

Abdelkader Chebli, Sarra Cherbal

No abstract is available for this record.

Sung-eun Heo, Manho Kim, Wijin Kim, Jongseok Choi · 12 authors

Biometric data has the potential to revolutionize health analytics and pharmacology by providing personalized insights into drug efficacy and health trajectories. However, its governance presents significant ethical challenges, particularly around individual ownership and privacy. This study addresses these challenges by proposing a sustainable and ethical framework that integrates biometric data with non-fungible tokens (NFTs). We developed a customized NFT framework with advanced smart contract functionalities that enhance privacy protection and decentralized authentication of biometric data ownership. This approach ensures the secure and ethical management of digital health data while reinforcing individuals' control over their biometric information. By leveraging cryptographic techniques for privacy protection, this framework enhances both the security and efficiency of personal health data management, offering a new perspective on ownership in digital health. Furthermore, a sustainable economic model is proposed to facilitate ethical transactions of tokenized biometric data within the NFT marketplace. The implications of this study extend beyond technology and commerce, offering valuable insights into human behavior in emerging digital economies and contributing to the creation of a more sustainable and equitable digital health ecosystem.

M. Sathyanarayanan, Usha Bala Varanasi, Inderpreet Kaur, Rajkumar Chadge · 6 authors

Online growth leads organizations to demand protected systems that protect privacy while managing identities. Traditional identity systems maintain centralized control that exposes users to data breaches while requiring new security solutions. A blockchain-enabled identity management solution was designed to implement zero-knowledge proofs (ZKP) for authentication methods with distributed execution of user credentials. The system uses Ethereum alongside Hyperledger Fabric platforms and runs simulations through Hyperledger Caliper platforms. The results demonstrate significant improvements in key performance metrics: The system delivered verification accuracy at 98.7% privacy leakage reached 0.05% while transaction latency fell under 125 ms and TPS scalability reached 950. The proposed model delivered superior privacy guarantees and operating efficiency. Future advancements in decentralized identity management build upon a reliable platform that ensures both privacy preservation and secure identity solutions.

Chitrita Devi, R. R. Shantha Spandana, G.V.T. Swapna, G Viswanath

This project provides a Cloud-Assisted Decentralized privacy-preserving Framework (CA-DPPF) that amalgamates cloud computing, blockchain generation, and IPFS to tackle the complexities of securely and efficaciously storing sensitive healthcare data. The framework utilizes ECDSA digital signatures and RSA encryption to assure strong person authentication and statistics safety, in accordance with present day developments in safeguarding healthcare information. IPFS is applied for scalable storage solutions, addressing the limitations of traditional centralized cloud services, as indicated in previous research. Blockchain era augments the system through supplying immutable document-preserving, mitigating the weaknesses of centralized systems. A rankings module is incorporated to guarantee the legitimacy of healthcare feedback, allowing people to assess doctors, with these checks securely documented on the blockchain to prevent manipulation. smart contracts, created in Solidity, enable secure transactions and govern user data at the Ethereum blockchain, making certain transparency and integrity in all interactions. The studies gives a spread that integrates the CHACHA20 encryption algorithm, strengthening computational efficiency and safety while complementing present encryption methods and improving usual system overall performance.

A. L. Afzal

Federated Learning (FL) enables collaborative model training across hospitals while keeping patient data local, thus aiming to satisfy strict healthcare privacy regulations (e.g. HIPAA, GDPR). However, FL still leaks information via shared model updates, exposing it to membership inference and gradient inversion attacks. In this work, we propose an end-to-end framework that integrates zero-knowledge proofs (ZKPs) with FL to ensure both data privacy and trust in the aggregation process. In our design, each hospital (client) sends encrypted model updates to a central aggregator, which then computes the global model and simultaneously generates a succinct ZKP (e.g. a zk-SNARK) attesting to the correctness of the aggregation. Clients (or a verifier network) can efficiently verify this proof without learning any additional information. We simulate a disease-prediction task on synthetic medical data and evaluate metrics including predictive accuracy, proof generation/verification time, and communication overhead. Our results (see Table 1 and Fig. 3) show that incorporating ZKP maintains almost identical model accuracy compared to standard FL while adding moderate computational and bandwidth overhead. ZKP verification costs scale favorably (often <50% of proof generation time) and can be offloaded to a blockchain network to avoid burdening resource-constrained hospitals. The key contribution is a structured ZK-FL framework combining FL and zk-SNARKs, along with a formal threat model. This approach closes FL’s trust gap in healthcare settings, and suggests future work on scalable proof systems (e.g. post-quantum ZKPs) and integration with blockchain-based verifiers.

Chaitanya Rahalkar, Anushka Virgaonkar

Bitcoin is a decentralized, pseudonymous ledger-based cryptocurrency where all transactions are public. Over time, various privacy-preserving techniques have emerged to address the de-anonymization of Bitcoin users. Meanwhile, newer cryptocurrencies have been developed with enhanced privacy features. This paper evaluates major privacy-focused strategies in Bitcoin and beyond, focusing on techniques still relevant as of 2025. We summarize well-known privacy attacks that de-anonymize Bitcoin, and analyze countermeasures deployed or proposed to protect user privacy. We also examine privacy mechanisms in other cryptocurrencies (e.g., Monero, Zcash) and compare their effectiveness. Finally, we discuss the current state of privacy in cryptocurrency usage, including key technical and regulatory challenges.

L V Vedashree, Yogesh Ramaswamy, Leeladhar Gudala, Haydeer MohamadAbbas · 5 authors

Nowadays, the increasing adoption of cloud computing has raised concerns about data privacy and security. However, the existing Homomorphic Encryption (HE) model has limited scalability which led to time-consuming in large-scale cloud computing environments. Hence, this research proposes Decentralized Blockchain-based Authentication with Zero-Knowledge Cloud Auditing (DBA-ZKCA) to improve data security and integrity in cloud computing environments. The proposed DBA-ZKCA consists of five key phases to ensure the privacy, authentication and secure data communication. The first phase initialization sets up cryptographic keys and identity verification parameters before cloud operations starts. These operations are executed by Cloud Service Providers (CSP) and Revocation Admin (RA) respectively. Then, cloud consumer registration phase allows only authorized users to access the cloud services. After that, authentication based on anonymous access phase utilizes DBA to store immutable authentication records. Next, the secure cloud communications phase with ZKCA where the Zero-Knowledge Proofs (ZKP) allow cloud users to verity data integrity without revealing actual data. Finally, revocation phase utilizes RA to revoke access for unauthorized users and updates authentication records for preventing future access. From the results, the proposed DBAZKCA achieved better results when compared to existing Federated Learning and Cryptography (FLC) in terms of accuracy (98.9%) respectively.

Sara Batal, Said Hraoui, Mohammed Berrada

AI has already begun to lead transformation across industries, along with AI APIs such as predictive modeling and Natural Language Processing. Just like them, AI applications are on the rise as well. Unfortunately, those typically operate under the hackneyed idea of having a universal centralized access control entity, which are not only prone to security breaches, rather they lack efficiency and access transparency as well. Consequently, this hinders attempts made to secure sensitive data and enforce accountability in AI systems. This paper proposes a new system model based on blockchain technology and smart contracts and discusses how it manages access control to AI APIs. This solution tackles the limitations of conventional systems such as security, scalability, and transparency brought by decomposing access rights. There are different possibilities for monitoring and enforcing security policies, One of them, Dynamic access control prevents unauthorized users from gaining access to any resource. In addition, blockchain guarantees auditing by virtue of its decentralized nature, eliminating reliance on central entities. The proposed approach is substantiated through a combination of simulations alongside proof-of-concept implementations, demonstrating the ability to decrease access latency, enhance resistance to rogue access attempts and access management processes. This work increasingly enhances the decentralized security solutions available by providing an effective solution for access control in the context of centralized AI API’s for both enterprise and government use cases being scalable and robust as well as transparent.

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

Ensuring privacy and confidentiality in healthcare data management remains a critical challenge. Traditional centralized access control mechanisms are susceptible to security breaches, including unauthorized access, data leakage, and single points of failure, as well as privacy violations such as patient record exposure and improper data sharing. To address these issues, this paper proposes ACHealthChain, a blockchain-based framework leveraging Hyperledger Fabric for decentralized and transparent access control. The framework integrates the InterPlanetary File System (IPFS) for decentralized storage and ensures privacy through Hyperledger Fabric channels. ACHealthChain features PolicyChain for fine-grained access control and revocation, structuring patient health data into separate subchains for EHRs and diagnoses with permissioned access. Additionally, LogChain enhances auditing and accountability. A series of experiments evaluate ACHealthChain's performance and scalability, considering metrics such as throughput, latency, and resource utilization. Results demonstrate that ACHealthChain improves throughput by 19.7% and reduces latency by 87%, outperforming existing frameworks built on the same platform. The scalability analysis further confirms the framework's capability to handle increasing workloads within an expanding blockchain network. ACHealthChain presents a promising solution for secure and efficient healthcare data sharing with potential real-world applications.

Priyadarshni Gupta, Praveen Kumar, Shivani Tripathi, Rajiv Misra

ABSTRACT The emergence of the Metaverse has introduced significant challenges in task offloading and data processing due to its virtual universe nature with immersive environments and a multitude of interconnected users and devices. The abundance of data in the Metaverse poses security challenges in local processing, necessitating traditional methods such as data transfer to Mobile Edge Computing (MEC) and subsequently to the cloud, thereby emphasizing security concerns. In this paper, a novel approach to address these challenges has been introduced: An Ethereum Blockchain‐based MEC framework uses smart contracts designed to ensure secure task offloading. It enables authentication in the Metaverse through smart contracts, followed by modeling the task offloading issue as a Markov Decision Process (MDP). To solve this MDP problem, a hybrid algorithm integrating Deep Q‐Networks (DQN) with Bidirectional Long Short‐Term Memory (Bi‐LSTM), known as BRL‐Net (Bi‐LSTM Reinforcement Learning Network), has been proposed. This framework enables secure and efficient task offloading in dynamic Metaverse environments. BRL‐Net outperforms Proximal Policy Optimization (PPO), achieving a 9.93% higher reward and greater stability. The BRL‐Net's performance across Blockchain consensus mechanisms shows Delegated Proof of Stake (DPoS) as the most efficient, reducing latency by 49.96%, increasing throughput by 10.48%, and lowering energy consumption by 50.24%, compared to Proof of Stake (PoS), thereby optimizing Metaverse performance.

S. Aarthi, K. Aravinthan, R. N. Ravikumar, N. Sivakumar · 5 authors

Data protection relies on cryptography to secure data across Blockchain, IoE, and Federated Learning systems. Strong cryptographic methods ensure confidentiality, authenticity, and integrity, safeguarding evolving digital security needs. Key techniques include symmetric and asymmetric encryption, hash functions, digital signatures, and zero-knowledge proofs. Cryptography enables secure protocols like TLS, homomorphic encryption, and differential privacy while addressing quantum-resistant security challenges, ensuring robust digital privacy solutions.

Yalan Wang, Liqun Chen, Yangguang Tian, Long Meng · 5 authors

Abstract The World Wide Web Consortium (W3C) has established standards for decentralized identities (DIDs) and verifiable credentials (VCs). A DID serves as a unique identifier for an entity, while a VC validates specific attributes associated with the DID holder. To prove ownership of credentials, users generate verifiable presentations (VPs). To enhance privacy, the W3C standards advocate for randomizable signatures in VC creation and zero-knowledge proofs for VP generation. However, these standards face a significant limitation: they cannot effectively verify cross-domain credentials while maintaining anonymity. In this paper, we present Anonymous Verifiable Presentations with Extended Usability (AVPEU), a novel framework that addresses this limitation through the introduction of a notary system. At the technical core of AVPEU lies our proposed randomizable message-hiding signature scheme. We provide both a generic construction of AVPEU and specific implementations based on Boneh–Boyen–Shacham, Camenisch–Lysyanskaya, and Pointcheval–Sanders signature. Our experimental results demonstrate the feasibility of these schemes.

Damary Sabrina Amaracitra, Andry Alamsyah

Behind the prominence of Electronic Health Records (EHRs) in improving healthcare efficiency, their reliance on centralized infrastructure imposes significant challenges in security vulnerabilities, limited interoperability, and privacy concerns. To address these challenges, this study introduces a Decentralized Medical Record (DMR) model. The proposed model utilizes the decentralized nature of blockchain and integrates composable Non-Fungible Tokens (NFTs) to enable granular data access control. While the integration of blockchain has been studied, the integration of composable NFT in medical records is yet to be explored. The modular design of composable NFT leaves the potential to prevent unnecessary data exposure, ensuring medical data is secure and interoperable throughout treatment collaboration among diverse stakeholders while preserving patient privacy. By incorporating insurance participation in the healthcare ecosystem, this model enables personalized risk assessment. Through Multi-Criteria Analysis (MCA), the model evaluation focuses on six key aspects: functionality, benefit, potential, accessibility, credibility, and continuity. Using Simple Additive Weighting (SAW) under this methodology, we analyze stakeholder inputs and achieve an overall score of 0,868 indicating a positive rating in addressing stakeholder needs. Our findings contribute to medical data management by demonstrating a novel application of composable NFTs within a structured DMR framework, detailing its mechanism, NFT hierarchy, database architecture, and workflow.

Tianyu Zhang, Shen Dong, Öykü Deniz Köse, Yanning Shen · 5 authors

With the rise of machine learning techniques, ensuring the fairness of decisions made by machine learning algorithms has become of great importance in critical applications. However, measuring fairness often requires full access to the model parameters, which compromises the confidentiality of the models. In this paper, we propose a solution using zero-knowledge proofs, which allows the model owner to convince the public that a machine learning model is fair while preserving the secrecy of the model. To circumvent the efficiency barrier of naively proving machine learning inferences in zero-knowledge, our key innovation is a new approach to measure fairness only with model parameters and some aggregated information of the input, but not on any specific dataset. To achieve this goal, we derive new bounds for the fairness of logistic regression and deep neural network models that are tighter and better reflecting the fairness compared to prior work. Moreover, we develop efficient zero-knowledge proof protocols for common computations involved in measuring fairness, including the spectral norm of matrices, maximum, absolute value, and fixed-point arithmetic. We have fully implemented our system, FairZK, that proves machine learning fairness in zero-knowledge. Experimental results show that FairZK is significantly faster than the naive approach and an existing scheme that use zero-knowledge inferences as a subroutine. The prover time is improved by 3.1x--1789x depending on the size of the model and the dataset. FairZK can scale to a large model with 47 million parameters for the first time, and generates a proof for its fairness in 343 seconds. This is estimated to be 4 orders of magnitude faster than existing schemes, which only scale to small models with hundreds to thousands of parameters.

Mingzhi Mao, Xiangyuan Zhu, Jianguo Chen, Longxin Zhang · 6 authors

Due to the absence of a trusted model parameter sharing mechanism, model tampering, malicious uploads, and data inconsistency pose significant risks within the Federated Learning (FL) architecture. To address these challenges, this paper proposes an automated Smart Contract-based Asynchronous Model Aggregation (SCAMA) method to enhance model security in decentralized FL frameworks. We design a trigger-based asynchronous model submission mechanism driven by smart contracts. These contracts incorporate a dynamic adjustment strategy that can initiate global aggregation in real time. Furthermore, a decentralized parameter validity verification rule is embedded within the smart contract to ensure that submitted model updates align with the global optimization objective, thereby mitigating risks such as malicious uploads and training drift. Building upon this, we introduce a trustless model parameter-sharing strategy based on on-chain storage. The global model update process is encoded into the smart contract and executed through multiparty verifiable storage on a distributed ledger. During model aggregation, the smart contract utilizes encrypted hashing to verify the consistency of submitted models, ensuring that the parameter versions received by each node are traceable, reliable, and tamper-proof. Experimental results demonstrate that our method offers significant improvements in computational efficiency and model convergence speed compared with traditional synchronous FL approaches and existing asynchronous strategies.

Nuo Xu, Tianxiu Xie, Kai Ding, Keke Gai

Recent digital transformation across industries has extended the demands for secure cross-domain transactions and data sharing. However, traditional Public Key Infrastructure (PKI)-based systems encounter challenges deriving from implementing centralized identity management setting, such as single-point failures, data silos, and insufficient privacy safeguards. Blockchain-based Decentralized Identity (DID) schemes have emerged as a promising solution to addressing cross-domain authentication challenges. In this work, we have systematically examined DID applications in cross-domain authentication, analyzing core mechanisms, e.g., privacy-preserving techniques, anonymous credentials, and lightweight verification protocols. We also investigate a few key supportive technologies, including Zero-Knowledge Proofs (ZKP) and cryptographic accumulators. Main findings of this work covers identifying consensus mechanism limitations and suggesting future potential research directions.

Awid Vaziry, Sandro Rodriguez Garzon, Patrick Herbke, Carlo Segat · 5 authors

The intersection of blockchain (distributed ledger) and identity management lacks a comprehensive framework for classifying distributed-ledger-based identity solutions. This paper introduces a methodologically developed taxonomy derived from the analysis of 390 scientific papers and expert discussions. The resulting framework consists of 22 dimensions with 113 characteristics, organized into three groups: trust anchor implementations, identity architectures (identifiers and credentials), and ledger specifications. This taxonomy facilitates the systematic analysis, comparison, and design of distributed-ledger-based identity solutions, as demonstrated through its application to two distinct architectures. As the first methodology-driven taxonomy in this field, this work advances standardization and enhances understanding of distributed-ledger-based identity architectures. It provides researchers and practitioners with a structured framework for evaluating design decisions and implementation approaches.

Mallikarjun Bellundagi

The rapid growth of intelligent systems has raised significant concerns regarding data privacy and security. Traditional centralized machine learning approaches require data aggregation, increasing the risk of data breaches and regulatory violations. Federated Learning (FL) has emerged as a promising paradigm that enables collaborative model training while keeping data decentralized. This paper presents a comprehensive study of federated learning for privacy-preserving intelligent systems, highlighting its architecture, methodologies, applications, and challenges. The study also proposes an adaptive federated framework integrating secure aggregation and differential privacy. The findings demonstrate that federated learning significantly enhances privacy while maintaining model performance, making it suitable for healthcare, finance, and IoT applications.

Malak Al-Majali, Mohammad Aljaidi, Qais Al-Na’amneh, Ghassan Samara · 6 authors

The surge in smartphone users has fueled the development of mobile Cloud Computing (MCC), a technology integrating cloud computing's power with mobile device mobility. MCC offers on-demand access to cloud resources like storage, processing power, and software, enhancing mobile device functionality and efficiency. This paper delves into user privacy concerns in 5G MCC environments. The convergence of mobile computing, wireless networks, and cloud computing has fostered the rapidly evolving field of MCC. Advancements in 5G mobile networks are expected to propel MCC services even further, making them a focal point in the mobile service landscape. However, 5G MCC also presents significant user privacy challenges, including network latency and data security. This paper investigates various cryptographic techniques employed to safeguard user privacy in 5G MCC systems. We compare and analyze encryption techniques, zero-knowledge proofs, MFA, and blockchain, and the inherent challenges associated with each approach.

Junhui Zhao, Yingxuan Guo, Longxia Liao, Dongming Wang

Vehicular Ad-hoc Network (VANET) is a platform that facilitates Vehicle-to-Everything (V2X) interconnection. However, its open communication channels and high-speed mobility introduce security and privacy vulnerabilities. Anonymous authentication is crucial in ensuring secure communication and privacy protection in VANET. However, existing anonymous authentication schemes are prone to single points of failure and often overlook the efficient tracking of the true identities of malicious vehicles after pseudonym changes. To address these challenges, we propose an efficient anonymous authentication scheme for blockchain-based VANET. By leveraging blockchain technology, our approach addresses the challenges of single points of failure and high latency, thereby enhancing the service stability and scalability of VANET. The scheme integrates homomorphic encryption and elliptic curve cryptography, allowing vehicles to independently generate new pseudonyms when entering a new domain without third-party assistance. Security analyses and simulation results demonstrate that our scheme achieves effective anonymous authentication in VANET. Moreover, the roadside unit can process 500 messages per 19 ms. As the number of vehicles in the communication domain grows, our scheme exhibits superior message-processing capabilities.

Rohit Rohit, Riya Sharma, Shreya Rai, Prakhar Singh · 5 authors

Land registry systems are essential for recording property ownership and transactions. Conventional system relies on centralized authorities. It maintains records of land ownership, which usually suffer from inefficiencies, lack of transparency, and susceptibility to fraud. In these systems, the process of transferring land ownership involves a lot of paperwork, lengthy verification procedures, and high transaction costs. It leads to delays and disputes. Blockchain technology offers a solution to address the shortcomings of conventional land registry systems like centralization, transparency, security. The paper proposes a blockchain-based land registration system employed on Ethereum. The smart contract of blockchain are developed in solidity programming language. It enables automated execution of land transactions that ensures trust value among all the stackholders. It eliminates manual intervention and reducing the risk of fraud and errors. Land ownership records are kept on InterPlanetary File System (IPFS) which is used for decentralized file storage. The proposed system promises to improve security, simplify processes, ensure trust among stakeholder. It provide transparent and immutable records of transaction and ownership.