Blockchain Papers

Prakash Chandra Sharma, Rashid Mahmood, Hiral Raja, Narendra Singh Yadav · 6 authors

No abstract is available for this record.

Yikai Liu, Feng-lan Ju, Qunwei Zhang, Meng Zhang · 8 authors

The Internet of Things provides convenience to health systems, especially for remote monitoring of patient physical indicators. While providing convenience, there may be more security vulnerabilities in protecting patient and doctor information and storing health data effectively. As an important research branch in the field of the Internet of Things, the Internet of Medical Things is important for the overall improvement of public health in terms of how to safely conduct technology development and application research and to effectively implement healthcare needs. Blockchain technology is decentralized and untrusted as well as prevents tampering with data and reduces the cost of trust. Its good performance has a strong developmental nature in the healthcare field. This paper analyses how to solve security problems through access control under the Internet of Medical Things, and optimizes three access control methods. The Internet of Medical Things accesses control approach that introduces blockchain technology enhances computational and storage capabilities and is a good solution to the problem of third-party trustworthiness. Even in the face of the rapid growth of end devices, blockchain technology can solve some of the problems arising from access control of massive devices through three directions: hierarchical management, compressed storage and performance optimization. Finally, it provides directions for future research on the security aspects of blockchain technology under the Internet of Medical Things.

Chu‐Hsing Lin, Sin-Ping Li, Yu-Chiao Lin, Chiao-Hsu Tsai

A large number of patient images are generated every day in medical applications such as ultrasound, Computer Tomography scans, X-Ray, and so on. Medical data is related to patient privacy and personal rights. Thus, the security and privacy of medical images become an important issue. In order to comply with the regulations of the Personal Data Protection Act, medical images need to be protected under a secure method. Therefore, we proposed a secure storage method for chest X-Ray images from Kaggle based on blockchain technology. We developed a smart contract to control role-based access permissions and authenticate medical images. We performed a cryptographic operation on the X-Ray image after each medical examination and stored the image fingerprint in the blockchain. Artificial intelligence was used to identify pneumonia-related diseases. We compared the required X-Ray retrieval time with the conventional PACS systems. The experimental results showed that the overhead of the proposed scheme using blockchain was only about 5 %.

T. Sheeba, S Hemanth, Devaraj Verma C, A. N. Arularasan · 5 authors

Blockchain security via the Internet of Things (IoT) will reshape the decision-making function of the data-driven incumbent smart enterprise, providing the vision of the connected world of things. Enterprise IoT development of devices, personnel, and systems in such a way that they may connect and communicate with each other through the Internet. Blockchain is an enterprise financial transaction, and its digital network is distributed transaction ledger. Today, enterprises need the massive global data management and rapid trading volume to keep things going and growing. It creates enterprise business challenges of different types of security, transparency, and complexity of the problem. Enterprise architecture offers several advantages for the thief to obtain a specific user account, application, and access to the device. This is, will doesn't be to provide the necessities of security. The proposed Digital Hash Data Encryption (DHDE) is used to secure the transaction data-based embedded system people and blockchain. Blockchain and IoT technology integration may bring numerous benefits to mention. Therefore, the proposed DHDE algorithm comprehensively discusses the blockchain technology integration system. The proposed DHDE algorithm encrypts the transaction data for an unauthorized person who cannot access the enterprise transaction data based on embedded system people and blockchain.

Farah Mohammad, Saad Al-Ahmadi, Jalal Al‐Muhtadi

Brain tumors are nonlinear and present with variations in their size, form, and textural variation; this might make it difficult to diagnose them and perform surgical excision using magnetic resonance imaging (MRI) scans. The procedures that are currently available are conducted by radiologists, brain surgeons, and clinical specialists. Studying brain MRIs is laborious, error-prone, and time-consuming, but they nonetheless show high positional accuracy in the case of brain cells. The proposed convolutional neural network model, an existing blockchain-based method, is used to secure the network for the precise prediction of brain tumors, such as pituitary tumors, meningioma tumors, and glioma tumors. MRI scans of the brain are first put into pre-trained deep models after being normalized in a fixed dimension. These structures are altered at each layer, increasing their security and safety. To guard against potential layer deletions, modification attacks, and tempering, each layer has an additional block that stores specific information. Multiple blocks are used to store information, including blocks related to each layer, cloud ledger blocks kept in cloud storage, and ledger blocks connected to the network. Later, the features are retrieved, merged, and optimized utilizing a Genetic Algorithm and have attained a competitive performance compared with the state-of-the-art (SOTA) methods using different ML classifiers.

Praneeth. M. S, Adithya Parashar. K .C, Rajesh Kokkiligadda, R. Niyam · 8 authors

An NFT (Non-Fungible Token) image generator is a tool that allows users to generate unique, digital images that can be stored and traded as NFTs on blockchain technology. The images generated can range from simple illustration to complex digital artwork and can be bought and sold as one-of-a-kind collectibles, with their own secured and recorded on the blockchain. They are one-of-a-kind, verifiably owned and their ownership is recorded immutably on the blockchain. An NFT (Non-Fungible Token) image generator is a platform that creates unique, digital images or artworks that can be tokenized and traded as NFTs on blockchain technology. NFTs will hasten the expansion of cryptocurrencies outside the financial sector and provide fresh perspectives and ideas from a variety of producers, artists, digital goods collectors, developers, and others.

Naseem us Sehar, Osman Khalid, Imran Ali Khan, Faisal Rehman · 7 authors

Recently, researchers have applied blockchain technology in vehicular networks to take benefit of its security features, such as confidentiality, authenticity, immutability, integrity, and non-repudiation. The resource-intensive nature of the blockchain consensus algorithm makes it a challenge to integrate it with vehicular networks due to the time-sensitive message dissemination requirements. Moreover, most of the researchers have used the Proof-of-Work consensus algorithm, or its variant to add a block to a blockchain, which is a highly resource-intensive process with greater latency. In this paper, we propose a consensus algorithm for vehicular networks named as Vehicular network Based Consensus Algorithm (VBCA) to ensure data security across the network using blockchain that maintains a secured pool of confirmed messages exchanged in the network. The proposed scheme, based on a consortium blockchain, reduces average transaction latency, and increases the number of confirmed transactions in a decentralized manner, without compromising the integrity and security of data. The simulation results show improved performance in terms of confirmed transactions, transaction latency, number of blocks, and block creation time.

Zhifeng Wang, Wanxuan Wu, Chunyan Zeng, Jia‐Long Yao · 6 authors

With the development of blockchain technology, more and more attention has been paid to the intersection of blockchain and education, and various educational evaluation systems and E-learning systems are developed based on blockchain technology. Among them, Ethereum smart contract is favored by developers for its ``event-triggered" mechanism for building education intelligent trading systems and intelligent learning platforms. However, due to the immutability of blockchain, published smart contracts cannot be modified, so problematic contracts cannot be fixed by modifying the code in the educational blockchain. In recent years, security incidents due to smart contract vulnerabilities have caused huge property losses, so the detection of smart contract vulnerabilities in educational blockchain has become a great challenge. To solve this problem, this paper proposes a graph neural network (GNN) based vulnerability detection for smart contracts in educational blockchains. Firstly, the bytecodes are decompiled to get the opcode. Secondly, the basic blocks are divided, and the edges between the basic blocks according to the opcode execution logic are added. Then, the control flow graphs (CFG) are built. Finally, we designed a GNN-based model for vulnerability detection. The experimental results show that the proposed method is effective for the vulnerability detection of smart contracts. Compared with the traditional approaches, it can get good results with fewer layers of the GCN model, which shows that the contract bytecode and GCN model are efficient in vulnerability detection.

Xinyan Wang, Jingli Jia, Yuke Cao, Jiacheng Du · 7 authors

The currently researched trusted data storage encryption methods for metering assets store too little data and have long encryption time delays. In order to solve the above problems, the trusted data storage encryption method for metering assets based on blockchain technology is proposed. blockchain technology can carry out the underlying network data support for the trusted data storage encryption of metering assets, and its data validation technology can be used for metering asset data block processing, and the data analysis is used to achieve storage encryption by comparing and validating the asset measurement results in the Ethernet virtual contract through each node within the blockchain node zone. This paper mainly applies blockchain technology in data storage management system, briefly analyzes the underlying logic of blockchain technology and its application in data storage management system, the core of the system is the storage of information and data, with features such as "unforgeable", "traceable", "open and transparent" and "collective maintenance". The core of the system is the storage of information and data, with features such as "unforgeable", "traceable", "open and transparent", "collective maintenance", etc. Finally, we show the application results of the technology in the system.

Le Sun, Jin Wu, Yang Xu, Yanchun Zhang

No abstract is available for this record.

Yasir Afaq, Ankush Manocha

Recently, deep learning and blockchain technologies have gained successful attention due to the high potential of generating accurate decisions and data security, respectively. The data provenances characteristics such as transparency, traceability, and trustworthiness are provided by the vast majority of centralized server-based deep learning approaches. This article examines the advantages of combining deep learning algorithms with blockchain technology. In addition, the most effective strategy for combining these two technologies to achieve the best result is identified through the most recent state-of-the-art literature. In this manner, the article is divided into seven thematic taxonomies based on the literature review: applications of deep learning and blockchain, deep learning techniques, protocols, domains, types of blockchain, and datasets. We have outlined the advantages and disadvantages of blockchain-based deep learning frameworks to facilitate insightful discussions.

V. Vijayalakshmi, K. Sharmila

Blockchain is one of the decentralized processes in a worldview that works with parallel and distributed ledger technology, the application process, and service-oriented design. To propose a Secure data Transaction based on Hash coded Starvation Blockchain security using Padded Ring signature-ECC for Network of Things. Initially, the crypto policy is authenticated based on the user-owner shared resource policy and access rights. This creates a Public blockchain environment with a P2P Blockchain network. The owner encrypts the data using optimized ECC through Hash-coded Starvation Blockchain security (HCSBS). This makes the encrypted block's provable partition chain Link (P2CL). The encrypted blocks are transmitted into the network of nodes monitored by NoT. During the data transmission, the Network of Things monitors the transaction flow to verify the authenticity over the network of nodes. The monitored data be securely stored in transaction Block storage with the hash-indexed block with chain ring policy (HICLP). This creates controller node aggregation over the transaction environment to securely transfer the data to the peer end. The User gets the access Key to decrypt the data with policy aggregated shared resource policy to access the data. The proposed system produces high security as well compared to the previous design.

Esraa Elgamal, Walaa Medhat, Mohamed Abd Elfatah, Nashwa Abdelbaki

In recent years, advances in technology in several industries have resulted in massive data collections on the web. It raises worries about large data security and protection. The advent of Blockchain technology has caused a revolution in the security field for different applications. The distributed ledger is stored on each Blockchain node, which enhances security and data transparency. On the Blockchain network, illegal users are not authorized to undertake any fault transactions. In this article, we will discuss how Blockchain may be employed to secure the big data. We explain the problems that the Blockchain faced with big data and its solutions. We summarize recent works of Blockchain with big data and the present issues and trends. We demonstrate that Blockchain technology is still in its initial phases of validation and there are no large-scale application scenarios available, particularly in the big data sector. Finally, we narrow our study to the Healthcare industry and offer the following research directions for its primary issues.

K. M. Baalamurugan, P. Prabu, Nebojša Bačanin, K. Venkatachalam · 6 authors

Industrial Internet of Things (IIoT)-based systems have become an important part of industry consortium systems because of their rapid growth and wide-ranging application. Various physical objects that are interconnected in the IIoT network communicate with each other and simplify the process of decision-making by observing and analyzing the surrounding environment. While making such intelligent decisions, devices need to transfer and communicate data with each other. However, as devices involved in IIoT networks grow and the methods of connections diversify, the traditional security frameworks face many shortcomings, including vulnerabilities to attack, lags in data, sharing data, and lack of proper authentication. Blockchain technology has the potential to empower safe data distribution of big data generated by the IIoT. Prevailing data-sharing methods in blockchain only concentrate on the data interchanging among parties, not on the efficiency in sharing, and storing. Hence an element-based K-harmonic means clustering algorithm (CA) is proposed for the effective sharing of data among the entities along with an algorithm named underweight data block (UDB) for overcoming the obstacle of storage space. The performance metrics considered for the evaluation of the proposed framework are the sum of squared error (SSE), time complexity with respect to different m values, and storage complexity with CPU utilization. The results have experimented with MATLAB 2018a simulation environment. The proposed model has better sharing, and storing based on blockchain technology, which is appropriate IIoT.

Asma Belhadi, Jon-Olav Holland, Anis Yazidi, Gautam Srivastava · 6 authors

In the quest of training complicated medical data for Internet of Medical Things (IoMT) scenarios, this study develops an end-to-end intelligent framework that incorporates ensemble learning, genetic algorithms, blockchain technology, and various U-Net based architectures. Genetic algorithms are used to optimize the hyper-parameters of the used architectures. The training process was also protected with the help of blockchain technology. Finally, an ensemble learning system based on voting mechanism was developed to combine local outputs of various segmentation models into a global output. Our method shows that strong performance in a condensed number of epochs may be achieved with a high learning rate and a small batch size. As a result, we are able to perform better than standard solutions for well-known medical databases. In fact, the proposed solution reaches 95% of intersection over the union, compared to the baseline solutions where they are below 80%. Moreover, with the proposed blockchain strategy, the detected attacks reached 76%.

Sikandar Ali, Abdullah, Tagne Poupi Theodore Armand, Ali Athar · 9 authors

Digitization and automation have always had an immense impact on healthcare. It embraces every new and advanced technology. Recently the world has witnessed the prominence of the metaverse which is an emerging technology in digital space. The metaverse has huge potential to provide a plethora of health services seamlessly to patients and medical professionals with an immersive experience. This paper proposes the amalgamation of artificial intelligence and blockchain in the metaverse to provide better, faster, and more secure healthcare facilities in digital space with a realistic experience. Our proposed architecture can be summarized as follows. It consists of three environments, namely the doctor's environment, the patient's environment, and the metaverse environment. The doctors and patients interact in a metaverse environment assisted by blockchain technology which ensures the safety, security, and privacy of data. The metaverse environment is the main part of our proposed architecture. The doctors, patients, and nurses enter this environment by registering on the blockchain and they are represented by avatars in the metaverse environment. All the consultation activities between the doctor and the patient will be recorded and the data, i.e., images, speech, text, videos, clinical data, etc., will be gathered, transferred, and stored on the blockchain. These data are used for disease prediction and diagnosis by explainable artificial intelligence (XAI) models. The GradCAM and LIME approaches of XAI provide logical reasoning for the prediction of diseases and ensure trust, explainability, interpretability, and transparency regarding the diagnosis and prediction of diseases. Blockchain technology provides data security for patients while enabling transparency, traceability, and immutability regarding their data. These features of blockchain ensure trust among the patients regarding their data. Consequently, this proposed architecture ensures transparency and trust regarding both the diagnosis of diseases and the data security of the patient. We also explored the building block technologies of the metaverse. Furthermore, we also investigated the advantages and challenges of a metaverse in healthcare.

Rachna Jain, Geetika Dhand, Kavita Sheoran, Shaily Malik · 5 authors

Certificate management is a tedious task for any university or any other organization. These schemes impose problems in Public Key Infrastructure (PKI). Checking the validity and preserving the security of these documents is of utmost importance. In this work, we have devised a blockchain-based solution for preventing malfunctioning in certificate validation which is an important step for any university. Each certificate is uploaded in its hash format and is stored using blockchain. The hashes are stored in unique transactions in nodes, which are deployed on a private network. Using the SHA-256 hashing algorithm, the certificates are uploaded into the system and can be viewed by anyone with the right credentials. Due to the usage of blockchain technology, the certificates are stored in a decentralized manner, which ensures there is no central point of failure. Any changes in the uploaded document need to be validated by other nodes. This paper also improvises that when certificate uploading is required new nodes are added, instead of modifying the past blocks. This work provides a very user-friendly app where any user with the right credentials can upload documents. In this work, digitized documents are stored using Inter Planetary File System (IPFS) which is distributed method of storage. Our theoretical analysis proves that it is a user-friendly application with the security of blockchain technology in partnership with IPFS. Only the issuer can upload documents and others can only view them. Using our proposed solution, problem of malicious certificates can be tackled with E-certification. The proposed method solves all the issues of storing, validating, and sharing documents. Chaotic Map technique is used in hash generation which is quite simple to implement. The proposed approach Chaotic Key based Certificate validation (CK-Cert) provides a hassle-free solution for certificate managements since it better manages the block size as compared to previously proposed techniques (PBCert and CertChain) as discussed with the help of graphs.

Eric Appiah Mantey, Conghua Zhou, Joseph Henry Anajemba, Yasir Hamid · 5 authors

With the proliferation of privacy issues surrounding the Internet of Medical (IoMT) recommender system data, this study presents a Secure Recommendation and Training Technique (SERTT) which is contingent on a combination of both federated learning and blockchain approaches. Firstly, the study presents a new framework for recording, sorting, and transmission of IoMT data while incorporating blockchain to ensure that the IoMT data transmitted to cloud servers is not made vulnerable by the sharing of the original data. Secondly, by utilizing medical data, the study designs a Recommender Data Management Neural Architecture (REDMANA) which is based on federated learning and model searching training framework. The proposed technique guarantees that the model gradients which are trained by each node are not disclosed all through the universal training and modeling procedure. This makes the raw data inaccessible to either the IoMT data provider or the user. Considering that the model ensures that users can only obtain their necessary inquiries, neither medical data suppliers nor users can obtain access to raw data. Thus, it reduces the issues of safeguarding medical data sets to the issues of securing data processing. Using numerical analysis and experiments the proposed technique is compared with other existing techniques, the result shows that the proposed SERTT system is efficient and secures recommender data management training and modeling technique and that it performs previously designed techniques as compared.

Jiaming Chen

With the emergence of Ethereum smart contracts, the number of blockchain platforms and decentralized applications has increased rapidly. However, most of the existing blockchain networks operate in an isolated environment, which brings about the scalability and connectivity issues of the blockchain platform and limits its development. Blockchain cross-chain technology has attracted much attention since the birth of Bitcoin. Its purpose is to realize the interconnection between chains and increase the interoperability of blockchains. It is the essential capability of the future blockchain interconnection network. This article systematically analyzes and summarizes the implementation schemes and projects of cross-chain technology. Firstly, it introduces the background and development history of blockchain cross-chain technology and explains the basic scheme of the cross-chain mechanism. Next, it elaborates on the implementation of three cross-chain applications. In the summary section, the advantages and disadvantages of the cross-chain mechanism and multiple cross-chain applications are summarized. Finally, several future research directions of cross-chain technology are listed.

Sandeep Chaware, U. Patel, Soham Shinde, Pranjali Shinde · 5 authors

No abstract is available for this record.

Vimal Bibhu, Lipsa Das, Ajay Rana, Silky Sharma · 5 authors

No abstract is available for this record.

Kota Nakazawa, Yuki Samata, Hiroyoshi Miwa

No abstract is available for this record.

Abdulaziz Aljaloud

In smart systems context, the storage and distribution of health-critical data – medical images, test reports, clinical information etc. that is processed and transmitted via web portal and pervasive devices which requires a secure and efficient management of patients’ medical records. The reliance on centralized data centers in the cloud to process, store, and transmit patients’ medical records poses some critical challenges including but not limited to operational costs, storage space requirements, and importantly threats and vulnerabilities to the security and privacy of health-critical data. To address these issues, this research proposes a framework and provides a proof-of-the-concept named Patient-Centric Medical Image Management System (PCMIMS). The proposed solution PCMIMS utilizes the Ethereum blockchain and Inter-Planetary File System (IPFS) to enable secure and decentralized storage capabilities that lack in existing solution for patients’ medical image management. The PCMIMS design facilitates secure access to Patient-Centric information for health units, patients, medics, and third-party requestors by incorporating the Patient-Centric access control protocol, ensuring privacy and control over medical data. The proposed framework is validated through the deployment of a prototype based on smart contract executed on Ethereum TESTNET blockchain that demonstrates efficiency and feasibility of the solution. Validation results highlight a correlation between (i) number of transactions (i.e., data storage and retrieval), (ii) gas consumption (i.e., energy efficiency), and (iii) data size (volume of Patient-Centric medical images) via repeated trials in Microsoft Windows environment. Validation results also indicate computational efficiency of the solution in terms of processing three most common types of Patient-Centric medical images namely (a) Magnetic resonance imaging (MRI) (b) X-radiation (X-Rays), (c) Computed tomography (CT) scan. This research primarily contributes by designing, implementing, and validating a blockchain based practical solution for efficient and secure management of Patient-Centric medical image management in the context of smart healthcare systems.

Thavavel Vaiyapuri, K. Shankar, Surendran Rajendran, Sachin Kumar · 6 authors

Internet of Things (IoT) devices are becoming increasingly ubiquitous in daily life. They are utilized in various sectors like healthcare, manufacturing, and transportation. The main challenges related to IoT devices are the potential for faults to occur and their reliability. In classical IoT fault detection, the client device must upload raw information to the central server for the training model, which can reveal sensitive business information. Blockchain (BC) technology and a fault detection algorithm are applied to overcome these challenges. Generally, the fusion of BC technology and fault detection algorithms can give a secure and more reliable IoT ecosystem. Therefore, this study develops a new Blockchain Assisted Data Edge Verification with Consensus Algorithm for Machine Learning (BDEV-CAML) technique for IoT Fault Detection purposes. The presented BDEV-CAML technique integrates the benefits of blockchain, IoT, and ML models to enhance the IoT network’s trustworthiness, efficacy, and security. In BC technology, IoT devices that possess a significant level of decentralized decision-making capability can attain a consensus on the efficiency of intrablock transactions. For fault detection in the IoT network, the deep directional gated recurrent unit (DBiGRU) model is used. Finally, the African vulture optimization algorithm (AVOA) technique is utilized for the optimal hyperparameter tuning of the DBiGRU model, which helps in improving the fault detection rate. A detailed set of experiments were carried out to highlight the enhanced performance of the BDEV-CAML algorithm. The comprehensive experimental results stated the improved performance of the BDEV-CAML technique over other existing models with maximum accuracy of 99.6%.