Blockchain Papers

Imane Majdoub, Khalid Atmani

No abstract is available for this record.

I. R. Solomka, B. B. Liubinskyi

This article presents a minimal viable product (MVP) architecture and proof-of-concept implementation that leverages zero-knowledge proofs to conduct essential KYC checks on a blockchain network without disclosing sensitive user information. The design employs a trusted off-chain KYC provider to validate user credentials, then uses succinct cryptographic proofs, compiled and verified with Groth16, Circom, and snarkjs, to guarantee compliance on-chain. A single smart contract deployed on a test network (Sepolia) verifies these proofs while insulating personal data from public exposure. The article outlines a practical off-chain/on-chain data flow, discusses essential performance metrics such as proof generation time and gas costs, and describes limited user testing for qualitative feedback. By integrating regulated AML checks with privacy-oriented ZKP protocols, this work demonstrates that decentralized applications can satisfy stringent compliance standards while upholding the confidentiality of user identities.

Baseer Fatima, K. P. Kaliyamurthie

The utilization of Zero-Knowledge Proofs (ZKPs) in blockchain technology enhances privacy while simultaneously preserving transparency. Given that blockchain networks frequently elicit privacy concerns owing to the inherently public nature of transaction data, ZKPs present a viable solution by enabling parties to authenticate transactions without disclosing sensitive information. This study primarily concentrates on zk-SNARKs and zk-STARKs, which represent advanced iterations of ZKPs that enhance both privacy and scalability. By analyzing established blockchain protocols, such as Zcash and Ethereum, this research illustrates that ZKPs can effectively safeguard privacy while also facilitating scalability through mechanisms such as zkrollups, which consolidate multiple transactions into a single proof, thereby alleviating congestion on the blockchain. Additionally, ZKPs enhance the verification efficiency, thereby reducing the computational burden on blockchain networks and promoting expedited transactions. However, challenges such as computational overheads and regulatory hurdles persist, hindering the widespread implementation of ZKPs. Future research endeavors should focus on overcoming these challenges by developing more efficient algorithms and collaborating with regulatory authorities to establish clear guidelines for ZKP-based systems. The potential implications of ZKPs extend beyond blockchain technology, offering substantial advantages to sectors such as finance, healthcare, and identity management, in which secure and confidential transactions are paramount. In summary, although ZKPs possess the capacity to transform privacy within decentralized networks, further advancements are required to fully harness their potential and ensure their extensive adoption.

Shota Tokuda, Shohei Kakei, Yoshiaki Shiraishi, Shoichi Saito

No abstract is available for this record.

Alireza Arbabi, Ardeshir Shojaeinasab, Homayoun Najjaran

ABSTRACT This manuscript presents an exhaustive review of blockchain‐based mixing services, aiming to fill the existing gap between academic innovations and real‐world implementations. Starting with an identification of the core functionalities and techniques employed by mixing services, the paper delves into detailed explanations of these operational mechanisms. It further outlines an evaluation framework tailored for a rigorous assessment, highlighting the key vulnerabilities and strengths of various solutions. In addition, the study identifies potential attack vectors that compromise these services. The paper explores the dual nature of mixing services: while they contribute to the preservation of privacy—a cornerstone of blockchain technologies—they can also facilitate illicit activities. By addressing key research questions, this study not only offers a comprehensive overview of the current state of mixing services but also sets the stage for future academic discourse in this evolving field.

Saad Ahmed

Blockchain technology has become a game-changer in strengthening data security and transparency within decentralized systems. This study examines its role in tackling major challenges related to data integrity, access control, and auditability, with a focus on industries like finance, decentralized finance (DeFi), and supply chain management. Using data analysis, case studies, and expert opinions, the research highlights how blockchain provides a secure, transparent, and efficient framework for digital transactions. The findings reveal that blockchain enhances security by eliminating weaknesses found in traditional centralized systems. With features like immutable record-keeping, cryptographic encryption, and decentralized control, blockchain ensures transactions remain secure, tamper-proof, and resilient against fraud and cyber threats. Additionally, its ability to maintain a shared, verifiable ledger fosters transparency and builds trust among stakeholders. Smart contracts further improve efficiency by automating processes and ensuring compliance with predefined rules. However, despite its many benefits, blockchain adoption faces hurdles such as scalability challenges, regulatory uncertainties, and integration difficulties. To overcome these barriers, the study suggests developing clear regulatory guidelines, implementing advanced scalability solutions, increasing awareness and technical training, promoting cross-industry collaboration, and adopting hybrid blockchain models that balance security with privacy. In conclusion, this research emphasizes blockchain’s potential to reshape data security and transparency across various sectors. By addressing existing challenges, blockchain can drive innovation, streamline operations, and enhance trust in digital ecosystems.

Myriam Hernández-Álvarez, Edgar Torres-Hernández

No abstract is available for this record.

David Krause

No abstract is available for this record.

Burhan Ul Islam Khan, Khang Wen Goh, Abdul Raouf Khan, Megat F. Zuhairi · 5 authors

A typical Wireless Sensor Network (WSN) defines the usage of static sensors; however, the growing focus on smart cities has led to a rise in the adoption of mobile sensors to meet the varied demands of Internet of Things (IoT) applications. This results in significantly increasing dependencies towards secure storage and effective resource management. One way to address this issue is to harness the immutability property of the Ethereum blockchain. However, the existing challenges in IoT communication using blockchain are noted to eventually lead to symmetry issues in the network dynamics of Ethereum. The key issues related to this symmetry are scalability, resource disparities, and centralization risk, which offer sub-optimal opportunities for nodes to gain benefits, influence, or participate in the processes in the blockchain network. Therefore, this paper presents a novel blockchain-based computation model for optimizing resource utilization and offering secure data exchange during active communication among mobile sensors. An empirical method of trust computation was carried out to identify the degree of legitimacy of mobile sensor participation in the network. Finally, a novel cost model has been presented for cost estimation and to enhance the users’ quality of experience. With the aid of a simulation study, the benchmarked outcome of the study exhibited that the proposed scheme achieved a 40% reduced validation time, 28% reduced latency, 23% improved throughput, 38% minimized overhead, 27% reduced cost, and 38% reduced processing time, in contrast to the existing blockchain-based solutions reported in the literature. This outcome prominently exhibits fairer symmetry in the network dynamics of Ethereum presented in the proposed system.

Ammar Ahmed, M. Haseeb Javed, Junaid Nasir Qureshi, Hamayun Khan · 5 authors

Federated Learning has emerged as a promising paradigm for collaborative machine learning while preserving data privacy. Federated Learning is a technique that enables a large number of users to jointly learn a shared machine learning model, managed by a centralized server while training data remains on user devices. In recent years, along with the blooming of Machine Learning (ML)-based applications and services, ensuring data privacy and security has become a critical obligation. ML-based service providers are not only confronted with difficulties in collecting and managing data across heterogeneous sources but also challenges of complying with rigorous data protection regulations such as the General Data Protection Regulation (GDPR) Federated Learning is very important to reduce data privacy risks. Federated Learning is a scheme in which several consumers work collectively to unravel machine learning problems, with a dominant collector synchronizing the procedure. This paper reviews recent advancements in privacy-preserving techniques for federated learning from a machine-learning perspective. This paper investigates the potential of Federated Learning for privacy-preserving machine learning in domains like healthcare, finance and IOT, where data privacy is paramount. We explore existing techniques to enhance privacy, including differential privacy, secure aggregation, homomorphic encryption, federated learning with encrypted, meta-learning, machine learning, privacy-preserving techniques, blockchain technology, decentralized learning, federated averaging, data privacy, searchable encryption and zero-knowledge proofs. This paper concludes with future research directions to address ongoing challenges & further enhance the effectiveness & scalability of privacy-preserving federated learning.

S Chen

Currently, the advantages of Shapley value in explaining model decisions have made incentive mechanisms Shapley value contribution assessment based become major research focus. This approach ensures the effectiveness and fairness of contribution assessment algorithms. However, there is a significant issue regarding the reliability of the computational results. Additionally, traditional Shapley value-based contribution processes face an issue where computational complexity increases exponentially with the number of participants. Addressing these issues, this paper proposes a contribution assessment and reward framework based on Shapley value and smart contract technology using alliance blockchain. This framework overcomes the reliance on third-party institutions that is characterized by traditional incentive mechanisms, leveraging the decentralized nature of blockchain to eliminate trust issues associated with a single centralized entity. Furthermore, the traceability of blockchain ensures the transparency and traceability of the contribution assessment and reward distribution processes.

Xiaojun Yin, Xijun Wu, Xinming Zhang

Federated learning (FL) has gained significant attention in distributed machine learning due to its ability to protect data privacy while enabling model training across decentralized data sources. However, traditional FL methods face challenges in ensuring trust, security, and efficiency, particularly in heterogeneous environments with varying computational capacities. To address these issues, we propose a blockchain-based trusted federated learning method that integrates FL with consortium blockchain technology. This method leverages computational power registration to group participants with similar resources into private chains and employs cross-chain communication with a central management chain to ensure efficient and secure model aggregation. Our approach enhances communication efficiency by optimizing the model update process across chains, and it improves security through blockchain’s inherent transparency and immutability. The use of smart contracts for participant verification, model updates, and auditing further strengthens the trustworthiness of the system. Experimental results show significant improvements in communication efficiency, model convergence speed, and security compared to traditional federated learning methods. This blockchain-based solution provides a robust framework for creating secure, efficient, and scalable federated learning environments, ensuring reliable data sharing and trustworthy model training.

Peda Narayana Bathula, M. Sreenivasulu

The importance of secure data sharing in fog computing is increasing due to the growing number of Internet of Things (IoT) devices. This article addresses the privacy and security issues brought up by data sharing in the context of IoT fog computing. The suggested framework, called “BlocFogSec”, secures key management and data sharing through blockchain consensus and smart contracts. Unlike existing solutions, BlocFogSec utilizes two types of smart contracts for secure key exchange and data sharing, while employing a consensus protocol to validate transactions and maintain blockchain integrity. To process and store data effectively at the network edge, the framework makes use of fog computing, notably reducing latency and raising throughput. BlocFogSec successfully blocks unauthorized access and data breaches by restricting transactions to authorized nodes. In addition, the framework uses a consensus protocol to validate and add transactions to the blockchain, guaranteeing data accuracy and immutability. To compare BlocFogSec's performance to that of other models, a number of simulations are conducted. The simulation results indicate that BlocFogSec consistently outperforms existing models, such as Security Services for Fog Computing (SSFC) and Blockchain-based Key Management Scheme (BKMS), in terms of throughput (up to 5135 bytes per second), latency (as low as 7 ms), and resource utilization (70% to 92%). The evaluation also takes into account attack defending accuracy (up to 100%), precision (up to 100%), and recall (up to 99.6%), demonstrating BlocFogSec's effectiveness in identifying and preventing potential attacks.

Xin Wang, Li Jiaqian, Ding Xueshuang, H. Zhang · 5 authors

The problem of data privacy protection in the information age deserves people’s attention. As a distributed machine learning technology, federated learning can effectively solve the problem of privacy security and data silos. Differential privacy(DP) technology is applied in federated learning(FL). By adding noise to raw data and model parameters, it can further enhance the degree of data privacy protection. Over the years, differential privacy technology based on federated learning framework has been developed, which is divided into central differential privacy federated learning(CDPFL) and local differential privacy federated learning(LDPFL). Although differential privacy may reduce the accuracy and convergence of federated learning models while protecting data privacy, researchers have proposed a variety of optimization methods to balance privacy protection and model performance. This paper comprehensively expounds the research status of differential privacy techniques based on the federated learning framework, first providing detailed introductions to federated learning and differential privacy technologies, and then summarizing the development status of two types of federated learning differential privacy(DPFL) techniques respectively; for CDPFL, the paper divides the discussion into first proposal of CDP and typical application examples, the impact of Gaussian mechanisms on model accuracy, optimization based on asynchronous differential privacy, and insights from other scholars; for LDPFL, the paper divides the discussion into first proposal of LDP and typical application examples, processing multidimensional data and improving model accuracy, existing methods and optimization for reducing communication costs, balancing privacy protection and data usability, LDPFL based on the Shuffle model, and insights from other scholars; following this, the paper addresses and summarizes the unique challenges introduced by incorporating differential privacy into federated learning and proposes solutions; finally, based on a summary of existing optimization techniques, the paper outlines future directions and specifically discusses three research ideas for enhancing the optimization effects of federated differential privacy: advanced optimization strategies combining Bayesian methods and the Alternating Direction Method of Multipliers (ADMM), integrating lattice homomorphic encryption techniques from cryptography to achieve more efficient differential privacy protection in federated learning, and exploring the application of zero-knowledge proof techniques in federated learning for privacy protection.

Huda A. Ahmed, Hend Muslim Jasim, Ali Noori Gatea, Ali Amjed Ali Al-Asadi · 5 authors

Vehicular Ad-hoc Networks (VANETs) are growing into more desirable targets for malicious individuals due to the quick rise in the number of automated vehicles around the roadside. Secure data transfer is necessary for VANETs to preserve the integrity of the entire network. Federated learning (FL) is often suggested as a safe technique for exchanging data among VANETs, however, its capacity to protect private information is constrained. This research proposes an extra level of security to Federated Q-learning by merging Blockchain technology with VANETs. Initially, traffic data is encrypted utilizing the Extended Elliptic Curve Cryptography (EX-ECC) technique to enhance the security of data. Then, the Federated Q-learning model trains the data and ensures higher privacy protection. Moreover, interplanetary file system (IPFS) technology allows Blockchain storage to improve the security of VANETs information. Additionally, the validation process of the proposed Blockchain framework is performed by utilizing a Delegated Practical Byzantine Fault Tolerance (DPBFT) based consensus algorithm. The proposed approach to federated Q-learning offered by Blockchain technology has the potential to develop VANET safety and performance. Comprehensive simulation tests are performed with several assessment criteria considered for number of vehicles 100, Throughput (102465.8 KB/s), Communication overhead (360.57 Mb), Average Latency (864.425 ms), Communication Time (19.51 s), Encryption time (0.98 ms), Decryption time (1.97 ms), Consensus delay (50 ms) and Validation delay (1.68 ms), respectively. As a result, the proposed approach performs significantly better than the existing approaches.

Pavel P. Deshevov, Sergey S. Ukustov

Identification, authentication, and authorization processes can be conducted in various ways. Particular attention is given to the processes implemented within the self-sovereign identity paradigm. This paper analyses the processes from a data leak perspective. A comparison is made between self-sovereign identity and a centralized identity provider scheme. An overview of the relevant implementations for these processes is provided: in both the self-sovereign and non-sovereign paradigms. It has been found that, from the data leaks perspective, the self-sovereign identity scheme could only provide superior security if zero-knowledge proof technology is applied.

Deepti Rani, Nasib Singh Gill, Preeti Gulia, Mohammad Yahya · 8 authors

No abstract is available for this record.

Yuxuan Meng, Baosheng Wang, Qianqian Xing, Xiaofeng Wang · 6 authors

The massive data generated by the Internet of Things (IoT) is often outsourced to the cloud, leading to a separation between data ownership and management. Access control during the data’s validity period and assured deletion once that period expires are both crucial for protecting privacy. While recent research has primarily focused on access control, assured deletion has received less attention. Existing assured deletion schemes can be classified into key-control based and cryptographic policy based methods, but to varying degrees, they have limitations such as requiring a trusted third party, high encryption overhead, lack of support for deletion verification and fine-grained access control. To address these limitations, we propose BBAD, a blockchain-based assured deletion scheme that leverages smart contracts for fine-grained access control, employs Shamir secret sharing and re-encryption for assured key deletion, and utilizes Merkle Hash Tree (MHT) for public deletion verification. Notably, BBAD eliminates the need for a trusted third party, exhibits low computational overhead, supports customizable deletion time limit, and enables offline verification of deletion for users. Our experimental comparison with two prominent alternatives, Secure Electronic-Document Self-Destructing with Identity-Based Timed-Release Encryption (ESITE) and Key-Policy Attribute-Based Encryption for Assured Deletion (AD-KP-ABE), demonstrates that BBAD reduces data processing time by over 46.5%, data deletion time by 98.4%, and deletion verification time by 99.0%.

Sakshi Patni, Joohyung Lee

The development of medical data and resources has become essential for enhancing patient outcomes and operational efficiency in an age when digital innovation in healthcare is becoming more important. The rapid growth of the Internet of Medical Things (IoMT) is changing healthcare data management, but it also brings serious issues like data privacy, malicious attacks, and service quality. In this study, we present EdgeGuard, a novel decentralized architecture that combines blockchain technology, federated learning, and edge computing to address those challenges and coordinate medical resources across IoMT networks. EdgeGuard uses a privacy-preserving federated learning approach to keep sensitive medical data local and to promote collaborative model training, solving essential issues. To prevent data modification and unauthorized access, it uses a blockchain-based access control and integrity verification system. EdgeGuard uses edge computing to improve system scalability and efficiency by offloading computational tasks from IoMT devices with limited resources. We have made several technological advances, including a lightweight blockchain consensus mechanism designed for IoMT networks, an adaptive edge resource allocation method based on reinforcement learning, and a federated learning algorithm optimized for medical data with differential privacy. We also create an access control system based on smart contracts and a secure multi-party computing protocol for model updates. EdgeGuard outperforms existing solutions in terms of computational performance, data value, and privacy protection across a wide range of real-world medical datasets. This work enhances safe, effective, and privacy-preserving medical data management in IoMT ecosystems while maintaining outstanding standards for data security and resource efficiency, enabling large-scale collaborative learning in healthcare.

Ishu Sharma, Vikas Khullar

No abstract is available for this record.

Γεώργιος Σπαθούλας, Angeliki Katsika, Georgios Kavallieratos

No abstract is available for this record.

Zhang, Jango

With the rapid growth of blockchain technology, smart contracts are now crucial to Decentralized Finance (DeFi) applications. Effective vulnerability detection is vital for securing these contracts against hackers and enhancing the accuracy and efficiency of security audits. In this paper, we present SimilarGPT, a unique vulnerability identification tool for smart contract, which combines Generative Pretrained Transformer (GPT) models with Code-based similarity checking methods. The main concept of the SimilarGPT tool is to measure the similarity between the code under inspection and the secure code from third-party libraries. To identify potential vulnerabilities, we connect the semantic understanding capability of large language models (LLMs) with Code-based similarity checking techniques. We propose optimizing the detection sequence using topological ordering to enhance logical coherence and reduce false positives during detection. Through analysis of code reuse patterns in smart contracts, we compile and process extensive third-party library code to establish a comprehensive reference codebase. Then, we utilize LLM to conduct an indepth analysis of similar codes to identify and explain potential vulnerabilities in the codes. The experimental findings indicate that SimilarGPT excels in detecting vulnerabilities in smart contracts, particularly in missed detections and minimizing false positives.

Delowar Hossain, Quazi Mamun, Rafiqul Islam

Blockchain technology leverages a cryptographic system to provide secure and immutable storage of transaction histories within a decentralised framework. While various industries have demonstrated interest in integrating blockchain into their IT systems, concerns regarding accessibility, privacy, performance, and scalability persist. Permissioned blockchain frameworks offer a viable solution for securing confidential records. Extensive research has been conducted to explore the opportunities, challenges, application areas, and performance evaluations of different public and permissioned blockchain platforms. Given the sensitive nature of medical information, healthcare organisations must adhere to various legal obligations, including HIPAA regulations, to protect these data. Although navigating these requirements can be challenging, it is crucial for safeguarding the reputation of healthcare providers, maintaining patient trust, and avoiding legal repercussions. Permissioned blockchains represent decentralised digital ledgers tailored to collaborate among businesses and organisations. Their popularity has increased significantly in recent years, resulting in the availability of several leading options, such as Hyperledger Fabric, Corda, Quorum, and MultiChain. Each of these platforms presents its own set of advantages and disadvantages. Although blockchain technology remains relatively nascent in the permissioned realm, several factors warrant consideration when comparing these platforms. This study will review the existing landscape of blockchain technologies in healthcare applications and identify the research scopes. This research aims to determine how permissioned blockchain technology can effectively fulfil the requirements for managing healthcare data.

Zhongkai Lu, Lingling Wang, Zhengyin Zhang, Mei Huang · 6 authors

Federated learning is a widely used method for collaborative machine learning without sharing local data. In this approach, participants train models using their local data, and the model updates are aggregated into a global model. However, ensuring trustworthy model training is crucial because malicious participants may not use their actual local data or may not train the model as intended, which makes it challenging to guarantee the authenticity of the data and the integrity of the model training. To address these issues, we propose a trustworthy model training scheme (TMT-FL) with verifiable authenticity and integrity. Specifically, we leverage zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK) based proofs to verify the integrity of the training execution. To deal with the performance bottleneck in generating zk-SNARK proofs, we use the Chinese Remainder Theorem to optimize the convolution operation, and present an improved zk-SNARK based proof generating scheme which significantly reduces the online proving time. Besides, we adopt matrix commitment along with bloom filter to ensure the authenticity and integrity of the training datasets. Extensive experimental results demonstrate that our improved zk-SNARK scheme performs nearly$3.1\times$faster than the state-of-the-art in online proving time. Moreover, we experimentally confirm the efficiency of TMT-FL under diverse datasets in terms of computational costs, storage costs, and communication overheads.