Blockchain Papers

Manasa S. Desai, M. B. Nirmala, Yogesh Kumar, G. C. Varsha · 6 authors

No abstract is available for this record.

Yansong Cao, Yutong Wang, Jing Yang, Yonglin Tian · 6 authors

Isolated data islands are prevalent in intelligent automated optical inspection (AOI) systems, limiting the full utilization of data resources and impeding the potential of AOI systems. Establishing a collaborative ecology involving software providers, hardware manufacturers, and factories offers an encouraging solution to build a closed-loop data flow and achieve optimal data resource utilization. However, concerns about privacy issues, rights infringement, and threats from other participants present challenges in establishing an efficient and effective community. In this paper, we propose a novel framework, AOI-OPEN, which first creates a trustworthy AOI ecology to gather related entities with decentralized autonomous organization (DAO) mechanisms. Then, a parallel data pipeline is proposed to generate large-scale virtual samples from small-scale real data for AOI systems. Finally, federated learning (FL) is adopted to use the distributed data resources among multiple entities and build privacy-preserving big models. Experiments on defect classification tasks show that, with privacy preserved, AOI-OPEN greatly strengthens the utilization of distributed data resources and improves the accuracy of inspection models.

Omar Dib

Diabetic Retinopathy (DR) detection in distributed telemedicine environments requires secure, scalable, and privacy-preserving solutions. Traditional federated learning (FL) relies on a central server, raising concerns about data privacy and system trust. We propose a novel serverless framework, FL-BC-SMPC-SMOTE, that integrates deep learning, FL, secure multi-party computation (SMPC), the Synthetic Minority Over-sampling Technique (SMOTE), Blockchain (Hyperledger Fabric), and the InterPlanetary File System (IPFS) to address these challenges. Using the APTOS 2019 dataset, we trained CNN-based models (e.g., EfficientNet-B0, ResNet-18) across 2–10 clients, achieving approximately 90% accuracy without raw data sharing. SMPC eliminates the need for a central aggregator by distributing encrypted model updates among clients, enabling privacy-preserving learning. Blockchain ensures auditable and tamper-resistant aggregation, while IPFS significantly reduces communication overhead—from 64 GB to 100 KB per round. Local SMOTE enhances recall for minority classes by 10–15%, promoting equity in DR severity classification. Compared to differentially private baselines (52.18% accuracy), our framework delivers a robust balance of performance, privacy, and fairness. This GDPR/HIPAA-compliant solution offers a practical and trustworthy approach to decentralized DR detection in real-world telemedicine settings.

Abdul Razzaq, Muhammad Numair, Salman Ahmed, Muhammad Usman Akhtar

Advances in the healthcare sector need to secure efficient and decentralized systems to handle the large number of images and test reports. Traditional cloud-based healthcare systems pose crucial challenges, such as higher maintenance costs, significant storage demand, and, most importantly, privacy concerns. This chapter introduced the comprehensive patient-centric system for decentralized storage, secure medical data sharing, integrating blockchain technology and distributed file systems. The healthcare system leverages the IPFS and Ethereum blockchain to empowering scalable privacy protection of globally available storage of medical metadata. The framework enhances the accessibility and usability of incorporating a mobile offering for the patients, hospitals, and other stakeholders to interact with the decentralized system through the blockchain-integrated mobile application.This chapter provides an innovative approach to addressing the healthcare centralized data management limitations while ensuring patient privacy and data security.

Iraq Ahmad Reshi, Sahil Sholla

No abstract is available for this record.

Wassim Abbessi, Sofiane Ouni, Rabeb Ben Othmen, Wafa Badreddine · 5 authors

No abstract is available for this record.

Shikhar Singh, Bharat Bhushan, Mustafa Al-Asadi, Alaa Ali Hameed

No abstract is available for this record.

Frederico Chaves Carvalho, Marisa Maximiano, Ricardo Gomes, Vítor Távora · 6 authors

Increasing demand in innovative healthcare systems, as well as information management, enforces institutions and private consortiums to enroll in decentralized solutions that preserve patient’s sensitive information, and give capability of revoking and grating access to specific entities that request patient data. With blockchain emerging technology these solutions can be achieved allowing a more user-centric control of their own data. Furthermore, the need to conceal information and disabling data mining algorithm from agglomerating patient’s data and correlate them to their corresponding healthcare providers. This is crucial to maintain several privacy issues introduced by third parties accessing patient data without the patient’s explicit consent and applying those algorithms to perform clinical studies. This paper proposes an architectural approach at solving the problem of privacy preserving data sharing agreements between patients and healthcare providers, using blockchain, smart contracts and zero-knowledge proofs.

Faisal Alanazi, Mahdi Zareei, Alberto Rodríguez Arreola

The rapid growth of the Internet of Things (IoT) demands solutions that can secure massive streams of sensitive data without sacrificing performance. Traditional blockchains struggle in IoT environments, facing significant challenges with transaction speed, scalability, and privacy. This paper introduces PRIVOT, a novel blockchain architecture that integrates a Directed Acyclic Graph (DAG) for high-throughput consensus with lightweight zero-knowledge proofs (ZKPs) for confidential transactions, rateless coded computation for private analytics, and an AI-driven manager that dynamically balances security and efficiency. Our simulations show that PRIVOT significantly outperforms traditional blockchain approaches, achieving high transaction throughput (up to 480 TPS on a 500-device network) with confirmation latencies under 2.1 seconds, even under heavy load. The framework provides robust privacy, limiting data leakage to less than 0.1% against significant node collusion, while keeping computational overhead low enough for resource-constrained IoT devices. By unifying these techniques, PRIVOT offers a scalable and resilient solution ideal for large-scale IoT deployments where both high performance and strong privacy are paramount.

Mujahid Merchant

No abstract is available for this record.

Vinay Maurya, Vinay Rishiwal, Mano Yadav, Mohammad Shiblee · 7 authors

No abstract is available for this record.

Deepak Mishra, Pawan Singh Mehra

No abstract is available for this record.

Ehtisham Ul Haque, Waseem Abbasi, Ahmad Almogren, Jaeyoung Choi · 7 authors

The proliferation of Internet of Things (IoT) devices generates vast amounts of data, traditionally stored, processed, and analyzed using centralized systems, making them susceptible to attacks. Blockchain offers a solution by storing and securing IoT data in a distributed manner. However, the low performance and poor scalability of blockchain technology pose significant challenges for its application in IoT networks. The primary obstacle is the distributed consensus protocol, while ensuring data transparency, integrity, and immutability in a decentralized and untrusted circumstances which often compromises scalability. To address this issue, this paper introduces the use of the Delegated Proof of Stake (DPoS) consensus algorithm and sharding techniques to enhance scalability in blockchain-based IoT networks. Experimental results indicate that system throughput increases synchronously with the test load. Our findings reveal a tradeoff between throughput, latency, and up-downstream time on the Inter Planetary File System (IPFS). Given the critical importance of latency and throughput in IoT networks, the results demonstrate that DPoS offers high throughput, parallel processing, and robust security while efficiently scaling the network. Furthermore, at a test load of 500 Transactions Per Second (TPS), the system achieves a maximum throughput of approximately 11.094 ms. However, when the test load exceeds 2000 TPS, the total processing time for transactions extends to 11.205 ms. This method is particularly suitable for constrained IoT networks. Compared to previous edge computing-based approaches, our scheme demonstrates superior throughput performance.

Zhen Ling Teo, Xiaoman Zhang, Yechao Yang, Liyuan Jin · 15 authors

No abstract is available for this record.

Joe Davids, Mohamed El-Sharkawy, Hutan Ashrafian, Eric Herlenius · 5 authors

Abstract Background The use of Cloud-based storage personal health records has increased globally. The GPOC series introduces the concept of a Global Patient co-Owned Cloud (GPOC) of personal health records. Technical sandboxes allow the capability to simulate different scientific concepts before making them production ready. None exist for the medical fields and cloud-based research. Methods We constructed and tested the sandbox using open-source infrastructures (Ubuntu, Alpine Linux, and Colaboratory) and demonstrated it on a cloud platform. Data preprocessing utilised standard and in-house libraries. The Mina protocol, implementing zero-knowledge proofs, ensured secure blockchain operations, while the Ethereum smart contract protocol within Hyperledger Besu supported enterprise-grade sandbox development. Results Here, we present the GPOC series’ technical sandbox. This is to facilitate future online research and testing of the concept and its security, encryption, movability, research potential, risks and structure. It has several protocols for homomorphic encryption, decentralisation, transfers, and file management. The sandbox is openly available online and tests authorisation, transmission, access control, and integrity live. It invites all committed parties to test and improve the platform. Individual patients, clinics, organisations and regulators are invited to test and develop the concept. The sandbox displays co-ownership of personal health records. Here it is trisected between patients, clinics and clinicians. Patients can actively participate in research and control their health data. The challenges include ensuring that a unified underlying protocol is maintained for cross-border delivery of care based on data management regulations. Conclusions The GPOC concept, as demonstrated by the GPOC Sandbox, represents an advancement in healthcare technology. By promoting patient co-ownership and utilising advanced technologies like blockchain and homomorphic encryption, the GPOC initiative enhances individual control over health data and facilitates collaborative medical research globally. The justification for this research lies in its potential to improve evidence-based medicine and AI dissemination. The significance of the GPOC initiative extends to various aspects of healthcare, patient co-ownership of health data, promoting access to resources and healthcare democratisation. The implications include better global health outcomes through continued development and collaboration, ensuring the successful adoption of the GPOC Sandbox and advancing innovation in digital health.

Pyrros Chaidos, Aggelos Kiayias

No abstract is available for this record.

Minfeng Qi, Qin Wang, Zhipeng Wang, Manvir Schneider · 8 authors

In this article, we present the first Systematization of Knowledge (SoK) on constructing Layer Two (L2) solutions for Bitcoin. We carefully examine a representative subset of ongoing Bitcoin L2 solutions (40 out of 335 extensively investigated cases) and provide a concise yet impactful identification of six classic design patterns through two approaches (i.e., modifying transactions and creating proofs). Notably, we are the first to incorporate the inscription technology (emerged in mid-2023), along with a series of related innovations. We further establish a reference framework that serves as a baseline criterion ideally suited for evaluating the security aspects of Bitcoin L2 solutions, and which can also be extended to broader L2 applications. We apply this framework to evaluate each of the projects we investigated. We find that the inscription-based approaches introduce new functionality (i.e., programability) to Bitcoin systems, whereas existing proof-based solutions primarily address scalability challenges. Our security analysis reveals new attack vectors targeting data/state (availability, verification), assets (withdrawal, recovery), and users (disputes, censorship).

Nasim Paykari, Damian M. Lyons, Mohamed Rahouti

Creating an immutable repository for vital robot and environmental data, ensuring long-term accessibility, and functioning in the absence of GPS or mapping are crucial for visual homing navigation systems. We focus on the intersection of blockchain and robotics, particularly in visual homing. Our research involves an in-depth analysis of various blockchain consensus mechanisms, highlighting their suitability for visual homing applications. The heart of blockchain functionality lies in its consensus mechanism, which facilitates agreement among network nodes. In our first study part, we conduct a comprehensive comparative analysis of key consensus algorithms, emphasizing visual homing’s decentralization, fault tolerance, latency, and throughput requirements. This analysis serves as a valuable reference for researchers and developers, emphasizing the importance of aligning the chosen consensus mechanism with specific blockchain application needs. The second part of our work involves extensive experiments exploring the connection between blockchain and visual homing. We assess prominent consensus mechanisms like Proof of Work (PoW), Proof of Stake (PoS), Delegated Proof of Stake (DPoS), and Proof of Authority (PoA) within a virtual environment in Gazebo, leveraging wide area visual navigation (WAVN). Our research implementation is grounded in the ROS framework and the Gazebo simulation environment.

Keyi Guo, Haoyu Ren, Peiyu Wang

In recent years, blockchain technology has evolved significantly, enabling a decentralized network application model that offers both user anonymity and transparency. This unique characteristic of blockchain has led to its adoption in various sectors, including healthcare, finance, and transportation. The advancement of modern zero-knowledge proof technology has further enhanced blockchain's applications across these fields, bolstering privacy protection. Zero-knowledge proofs have become a key mechanism in blockchain smart contracts, offering a balance between transparency and privacy. Moreover, the integration of zero-knowledge proof technology with blockchain is facilitating technical advancements in areas facing challenges, such as autonomous driving technology. It is also addressing security concerns in more established technologies like the Internet of Things. This synergy between zero-knowledge proof and blockchain technologies is paving the way for innovative solutions across a wide range of applications.

Sushmita Ruj

Zero-knowledge proofs (ZKP) are used to prove the correctness of computations without revealing any other information. Zero-knowledge proofs have origins in Interactive proof systems which were introduced in the 1980’s. Last decade has seen a big leap from theory to practice, thanks to applications such as blockchains, anonymous credentials etc. Initially used for privacy preserving transactions in Zcash, these have been used in various ways in blockchain designs like Monero, ensuring anonymity of users, designing scalable Layer-2 solutions, verifiable computation in decentralised blockchain oracles and many more. These are being increasingly used in blockchain applications.The aims of this tutorial are as follows: (1) Provide background, history, evolution and theoretical foundations, (2) Discuss desirable properties of ZKP for blockchains and its applications, (3) Present some well known ZKP systems and show how they are used in blockchains, and (4) Introduce the audience to a myriad of open problems in this space.The tutorial will be self contained, no knowledge of cryptography or blockchain will be assumed.

Brijesh Kumar Chaurasia

No abstract is available for this record.

Abdullah M. Al‐Enizi, Shailendra Mishra, Abdullah Baihan

Blockchain and artificial intelligence are innovative technologies that can securely process and share data across unreliable networks. Due to data leakage from user information, it is critical to keep the data confidential and completely protected because criminals are looking for this information to attack the system or steal information in the banking sector. To address this issue, this paper suggests an Integrated Blockchain and Artificial intelligence (IBAI) Framework for secure financial transactions. A blockchain can store every customer's data in one place, while AI-driven algorithms can speedily examine that data and make an unbiased decision. The rising blockchain technology provides a decentralized architecture that enables the secure sharing of data and resources to the different networks and is promoted for removing centralized control and resolving the problems of AI. When suspicious behaviour happens, alerts can be triggered to avert theft. Furthermore, registration protocols based on AI are utilized to keep this data in comprehensive security. The numerical results show that the suggested IBAI model enhances the suspicious behaviour detection ratio and increases accuracy up to 98% compared to other models.

K Sattaiah, Kotadi Chinnaiah­

The term Blockchain is familiar to everyone in the world, it is an immutable, decentralized with peer to peer network and distributed hyper ledger system. So no one can tamper or hacking this transactions easily. In the world various types of blockchain methods are used that are private bockchain public blockchain and hybrid blockchain. Now these methods are implemented in all organizations for security reasons, e.g. Crypto currency, logistic, education and economic etc. The blockchain allows all types of transactions and data to be more secure. Decentralized hyper digital ledger is used to securely record transactions on the blockchain network using a hash function that should be consistency. These data transactions are completed with a consensus algorithm called Proof of Work (PoW) and Proof of Stake (POS) with the help of hash function related algorithm SHA256. In this paper, we are going to explain how to provide security in genesis node, subsequent nodes and Markel tree construction of blockchain using SHA-256 algorithm, cryptography and hash functions and implementation of security in all level of Blockchain using SHA-256.

George Vlahavas, Kostas Karasavvas, Athena Vakali

Abstract Since its inception in 2009, Bitcoin has become and is currently the most successful and widely used cryptocurrency. It introduced blockchain technology, which allows transactions that transfer funds between users to take place online, in an immutable manner. No real-world identities are needed or stored in the blockchain. At the same time, all transactions are publicly available and auditable, making Bitcoin a pseudo-anonymous ledger of transactions. The volume of transactions that are broadcast on a daily basis is considerably large. We propose a set of features that can be extracted from transaction data. Using this, we apply a data processing pipeline to ultimately cluster transactions via a k-means clustering algorithm, according to the transaction properties. Finally, according to these properties, we are able to characterize these clusters and the transactions they include. Our work mainly differentiates from previous studies in that it applies an unsupervised learning method to cluster transactions instead of addresses. Using the novel features we introduce, our work classifies transactions in multiple clusters, while previous studies only attempt binary classification. Results indicate that most transactions fall into a cluster that can be described as common user transactions. Other clusters include transactions made by online exchanges and lending services, those relating to mining activities as well as smaller clusters, one of which contains possibly illicit or fraudulent transactions. We evaluated our results against an online database of addresses that belong to known actors, such as online exchanges, and found that our results generally agree with them, which enhances the validity of our methods.