Blockchain data mining has the potential to reveal the operational status and behavioral patterns of anonymous participants in blockchain systems, thus providing valuable insights into system operation and participant behavior. However, traditional blockchain analysis methods suffer from the problems of being unable to handle the data due to its large volume and complex structure. With powerful computing and analysis capabilities, graph learning can solve the current problems through handling each node's features and linkage relationships separately and exploring the implicit properties of data from a graph perspective. This paper systematically reviews the blockchain data mining tasks based on graph learning approaches. First, we investigate the blockchain data acquisition method, integrate the currently available data analysis tools, and divide the sampling method into rule-based and cluster-based techniques. Second, we classify the graph construction into transaction-based blockchain and account-based methods, and comprehensively analyze the existing blockchain feature extraction methods. Third, we compare the existing graph learning algorithms on blockchain and classify them into traditional machine learning-based, graph representation-based, and graph deep learning-based methods. Finally, we propose future research directions and open issues which are promising to address.
This study aims to explore the construction of a personalized recommendation system (PRS) based on deep learning under the hybrid blockchain model to further improve the performance of the PRS. Blockchain technology is introduced and further improved to address security problems such as information leakage in PRS. A Delegated Proof of Stake-Byzantine Algorand-Directed Acyclic Graph consensus algorithm, namely PBDAG consensus algorithm, is designed for public chains. Finally, a personalized recommendation model based on the hybrid blockchain PBDAG consensus algorithm combined with an optimized back propagation algorithm is constructed. Through simulation, the performance of this model is compared with practical Byzantine Fault Tolerance, Byzantine Fault Tolerance, Hybrid Parallel Byzantine Fault Tolerance, Redundant Byzantine Fault Tolerance, and Delegated Byzantine Fault Tolerance. The results show that the model algorithm adopted here has a lower average delay time, a data message delivery rate that is stable at 80%, a data message leakage rate that is stable at about 10%, and a system classification prediction error that does not exceed 10%. Therefore, the constructed model not only ensures low delay performance but also has high network security performance, enabling more efficient and accurate interaction of information. This solution provides an experimental basis for the information security and development trend of different types of data PRSs in various fields.
Blockchain is a decentralized and tamper-proof distributed ledger technology. The consensus algorithm is one of the key technologies underlying blockchain. The Proof of Stake (PoS) consensus mechanism determines the next block producer on the blockchain based on the user's staked equity. Compared to Proof of Work (PoW), the Proof of Stake consensus mechanism solves the problem of wasted resources. However, the PoS consensus mechanism faces serious issues such as the coin age accumulation attack and the zero-cost benefit problem. Therefore, this paper proposes a Trust-Based Proof of Stake (TPoS) mechanism based on dynamic trustworthiness. TPoS divides nodes in the network into miner nodes and basic equity representative (shareholder) nodes, and assigns corresponding trustworthiness to nodes based on their participation in creating blocks. Shareholder nodes sign blocks and assign them trustworthiness, and finally compete for the weight of trustworthiness obtained by blocks to go on the chain. In addition, this paper analyzes the attack cost and system response to bribery attacks and common equity accumulation attacks. The results of simulation experiments show that the TPoS mechanism has significant advantages over traditional Proof of Stake mechanisms in dealing with bribery attacks and equity accumulation attacks.
Gifar Arif Haryadi, Allwinnaldo, Jae‐Min Lee, Dong‐Seong Kim
In the domain of healthcare management, conventional paper-based prescription systems manifest vulnerabilities. Addressing this, emerging solutions leverage blockchain and Non-Fungible Tokens (NFTs) to augment e-prescription processes. However, existing research needs comprehensive simulation insights into these NFT-based systems. This paper presents a pragmatic NFT-Integrated E-Prescription Management Smart Contract model to bridge this gap. The model capitalizes on blockchain’s security and NFTs’ attributes to enhance prescription traceability, ownership, and security. It streamlines prescription workflows, facilitating seamless interaction between healthcare providers and pharmacies while also introducing precise ownership control through NFTs. Implemented with a Role-Based Access Control system, authorization is exclusively granted to authorized entities, thereby bolstering security. A comparative analysis reveals distinct disparities in ownership management and prescription expiration. Furthermore, an assessment of cost-effectiveness and robust security measures, encompassing NFT integration, is conducted to safeguard sensitive healthcare data. The model’s applicability is demonstrated through public and local test network deployment. This paper addresses the existing research gap by furnishing comprehensive simulation insights, advancing the comprehension of NFT-based prescription systems.
With the rapid development of the Internet of Things (IoT), millions of IoT devices are constantly generating massive amounts of data. The development of artificial intelligence (AI) and edge computing makes it possible to conduct data analysis and knowledge mining efficiently among IoT edge devices. Knowledge, including intermediate results and training models obtained by large amounts of redundant data, is the core and foundation of edge intelligence. However, the sharing and utilization of knowledge still face a series of security and privacy issues, such as illegal knowledge access, knowledge tampering, and privacy leakage. To address these issues, in this article, we propose a blockchain-based knowledge storage and sharing architecture that enables secure knowledge management in intelligent IoT. We first design a permissioned blockchain-based decentralized and trusted knowledge storage scheme, which includes the on-chain encrypted knowledge storage and an improved Delegated Proof of Stake (DPoS) consensus protocol. Besides, we propose a lightweight attribute-based searchable encrypted knowledge-sharing mechanism, in which fine-grained and privacy-preserving knowledge collaboration is achieved through smart contracts and keyword search. Moreover, we reduce the computing overhead of edge devices through the design of partial outsourcing decryption. Finally, we analyze the security performance of our system as well as verify its practicality and ability to reject dishonest servers by simulation.
With the continuous development of Internet of Things (IoT) technology and artificial intelligence (AI) technology, the demand for Artificial Intelligence of Things (AIoT) edge applications is increasing. However, there are challenges in AIoT edge applications, such as limited resources of edge devices, data privacy leakage, inconsistent model deployment, device authentication, and data sharing difficulties, which can affect the security and intelligence level of AIoT edge applications. Therefore, we propose a trusted cloud–edge decision architecture that ensures trustworthy authentication of terminal devices. We use lightweight deep neural network training technology to run multilayer perceptron (MLP) models on resource-limited edge devices, reducing the difficulty of model design and development. We also introduce blockchain technology to enhance the security and privacy of model and data processing. We describe the four-layer architecture and corresponding workflow details, and we introduce the main data models and focus on the core technologies of the architecture. Finally, we completed the simulation verification of the model using carbon emissions data as a sample, demonstrating the feasibility and effectiveness of the model.
C. K. Shinzeer, Avinash Bhagat, Ajay Shriram Kushwaha
Governments and individuals have taken extraordinary measures to protect the health of the people during the COVID pandemic. Stored medical data remains the main target for hackers, and hence it needs to be stored securely. To achieve this objective, this paper proposes a novel model using Delegated Proof of Stake-Hyper ledger Fabric Block Chain (DPOS-HFBC). Primarily, by employing LL Subbandeigen Value decomposition employed Discrete Wavelet Transform (L2-DWT), the patient’s Lung Computed Tomography (CT) image data are collected and embedded. For embedding, the patient’s name and ID are taken. In embedding, a Pseudorandom number generator using the Mersenne twister algorithm employed in Elliptic Curve Cryptography (PM-ECC) is applied for key encryption. It covered the image that was embedded with the original and then stored in DPOS-HFBC. Likewise, for authorization, every patient’s biometric ID was hashed and stored in DPOS-HFBC. Data requesters request data in the Interplanetary File System (IPFS) of DPOS-HFBC, and the attributes from the request are extracted and sent to the authority for verification. After verifying, the authority shares their biometric ID with the requester and this gets hashed and then verified in DPOS-HFBC. To show the model’s supremacy, the proposed method was evaluated and compared with existing methods.
Brain Tumor Detection and Classification
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
In this paper, we focus on providing data provenance auditing schemes for distributed denial of service (DDoS) defense in intelligent internet of things (IoT). To achieve effective DDoS defense, we introduce a two-layer collaborative blockchain framework to support data auditing. Specifically, using data scattered among intelligent IoT devices, switch gateways self-assemble a layer of blockchain in the local autonomous system (AS), and the main chain with controller participation can be aggregated by its associated layer of blocks once a cycle, to obtain a global security model. To optimize the processing delay of the security model, we propose a process of data pre-validation with the goal of ensuring data consistency while satisfying overhead requirements. Since the flood of identity spoofing packets, it is difficult to solve the identity consistency of data with traditional detection methods, and accountability cannot be pursued afterwards. Thus, we proposed a Packet Traceback Telemetry (PTT) scheme, based on in-band telemetry, to solve the problem. Specifically, the PTT scheme is executed on the distributed switch side, the controller to schedule and select routing policies. Moreover, a tracing probabilistic optimization is embedded into the PTT scheme to accelerate path reconstruction and save device resources. Simulation results show that the PTT scheme can reconstruct address spoofing packet forward path, reduce the resource consumption compared with existing tracing scheme. Data tracing audit method has fine-grained detection and feasible performance.
With the expansion of scale, the Internet of Things (IoT) suffers more and more security threats, and vulnerability and sensitivity to attacks are also increasing. As a distributed and secure network architecture, Blockchain is suitable for protecting the security and privacy of the IoT. In this article, we propose a secure smart blockchain IoT architecture based on Graph Neural Networks (GNN) named GTxChain, using a distributed intelligent prophecy machine to obtain off-chain data and construct the transaction data structure of the blockchain through the blockchain-directed acyclic graph (DAG). In the off-chain transaction and off-chain storage part, we use the lightning network, improved IPFS and GNN to obtain transaction information and continuously update the blockchain network and blockchain for transaction verification and other operations. GTxChain employs an IPFS storage architecture to enhance user privacy and reduce data processing time. Compared to other blockchain architectures, it improves by 10.51% and has better efficiency and stability in terms of Merkle-proof time overhead. Experimental results show that the GTxChain architecture can effectively ensure the IoT’s trustworthiness, security, and privacy (TSP).
In recent years, the cryptocurrency market has been booming with an ever-increasing market capitalization. However, due to the anonymity of blockchain technology, this market has become a hotbed of financial crimes. As the largest blockchain platform supporting smart contracts, financial crimes including scams and hacking frequently happen on Ethereum and have caused serious losses. Therefore, it is necessary to classify Ethereum accounts in order to better identify those involved in illegal transactions and analyze the behavior patterns of different classes of accounts. In this paper, we construct an Ethereum transaction network based on transaction records and find that this network is with heterophily. However, most of the current work on account classification ignores the role of this heterophily information. We first figure out that the heterophily information of the neighborhood may also be beneficial for the final predictions. Based on this, we propose a new graph neural network (GNN) model, named BPA-GNN, which incorporates both homophilic and heterophilic information into the neighborhood aggregations. Specifically, BPA-GNN consists of three main modules including bi-path neighbor sampling, separated neighborhood aggregation, and attention-based node representation learning. Comprehensive experiments on a real Ethereum transaction dataset demonstrate the state-of-the-art performance of BPA-GNN, showing that the model can effectively extract and utilize neighborhood information to improve the distinguishability of node representations. As an effective solution for Ethereum account de-anonymization, BPA-GNN can help identify illegal activities and promote the healthy development of the Ethereum ecosystem.
Millions of individuals around the world have been impacted by the ongoing coronavirus outbreak, known as the COVID-19 pandemic. Blockchain, Artificial Intelligence (AI), and other cutting-edge digital and innovative technologies have all offered promising solutions in such situations. AI provides advanced and innovative techniques for classifying and detecting symptoms caused by the coronavirus. Additionally, Blockchain may be utilized in healthcare in a variety of ways thanks to its highly open, secure standards, which permit a significant drop in healthcare costs and opens up new ways for patients to access medical services. Likewise, these techniques and solutions facilitate medical experts in the early diagnosis of diseases and later in treatments and sustaining pharmaceutical manufacturing. Therefore, in this work, a smart blockchain and AI-enabled system is presented for the healthcare sector that helps to combat the coronavirus pandemic. To further incorporate Blockchain technology, a new deep learning-based architecture is designed to identify the virus in radiological images. As a result, the developed system may offer reliable data-gathering platforms and promising security solutions, guaranteeing the high quality of COVID-19 data analytics. We created a multi-layer sequential deep learning architecture using a benchmark data set. In order to make the suggested deep learning architecture for the analysis of radiological images more understandable and interpretable, we also implemented the Gradient-weighted Class Activation Mapping (Grad-CAM) based colour visualization approach to all of the tests. As a result, the architecture achieves a classification accuracy rate of 0.96, thus producing excellent results.
Security and trust have become the key issues in the Internet of Things (IoT) environment. Characterized by the centralized control and high-energy consumption, the traditional trust management schemes are not suitable for the IoT systems, in which most of the interactions are short-duration, random and maybe one-time, and the terminal devices always have resource constraints. Therefore, this article proposes a distributed and two-layered trust management framework based on blockchain architecture for IoT. The hierarchical architecture of the cloud, the edge, the IoT subgroups, and devices solves the resource limitation problem and improves the privacy protection of the IoT applications. And a novel lightweight$Q$-learning improved DPoS consensus algorithm named QV-DPoS is proposed to solve the problems of large energy consumption and high complexity of consensus mechanism of blockchain. Ethereum is used to build a blockchain-based IoT prototype system, and some experiments were designed to verify whether the proposed platform can successfully conduct trust management and achieve identity and behavior authentication between the IoT entities. Moreover, the simulation experiments based on NetLogo is also designed to test the performance of the trust and consensus mechanisms. The results of the experiments show that the proposed mechanisms can effectively enhance the credibility of the interactions in the IoT environments, improve the transaction success rate, and reduce energy consumption at least 10% compared with the traditional algorithms.
Smart contract is a trusted service provided on the blockchain, while cloud service is a traditional service mode with a large number of resources. The combination of the blockchain and cloud service is of great significance to the trusted expansion of services and the access to services inside and outside the blockchain. In this paper, we study the smart contract extension service in blockchain-cloud collaborative computing. The service module decoupling method of smart contract is proposed, and the parallel execution algorithm of smart contract service is designed, which improves the execution efficiency of smart contract service. Finally, this paper designs a secure data interaction method of smart contract and cloud service, which helps to maintain the data consistency between cloud computing and blockchain. The experimental results show that the proposed method can save at most 42.13% of the running time, and it can promote the data consistency between the cloud service and the blockchain.
Intelligent and sustainable healthcare systems can considerably benefit from applying Computational Intelligence (CI) and Artificial Intelligence (AI). These technological breakthroughs can reduce the ecological footprint and raise the bar for excellence. Yet, the broad adoption of such technologies for cutting-edge Internet of Things (IoT) applications generates enormous amounts of data, which can heavily strain the available computational resources. The major motivation behind this study is to provide evidence that Gated Recurrent Units (GRUs), a sophisticated subclass of Recurrent Neural Networks (RNNs), can outperform traditional RNNs. These technologies can be effective in identifying and treating breast cancer. This study collects data from tagged IoT devices and trains a GRU-RNN classifier. The Wisconsin Diagnostic Breast Cancer (WDBC) data tests the system’s accuracy. The results show the proposed Internet of Medical Things (IoMT) is more effective than the current methods in recall, accuracy, and precision while preserving 95% of the original GRU-RNN.
Multiaccess edge computing (MEC) network, as one of the key infrastructures of IoT, provides cloud computing capabilities at the edge of the radio access network (RAN) by integrating telecommunication and IT services. Integrating blockchain into the MEC network can provide users with secure, private, and traceable edge computing services at the near end, thereby improving IoT security, privacy, and automated use of resources. Due to some characteristics of the MEC network, there are still some challenges to integrate blockchain and edge computing into one system, especially the consensus algorithm of blockchain. The resources of edge computing nodes are limited, and the scale of the network is constantly expanding. Therefore, the blockchain consensus algorithm for the MEC network should occupy as little computing resources as possible, be green, and be permissionless. This article proposes a permissionless and scalable consensus algorithm “Hedera” for MEC network, which has the advantages of permissionless, security, decentralization, scalability, and greenness. The Hedera consensus algorithm is a hybrid blockchain consensus algorithm that combines the permissionless Proof-of-Capacity algorithm and the permissioned asynchronous Byzantine algorithm. This article tests the fairness, throughput, scalability, latency, and resource consumption of the algorithm by developing and deploying a prototype system. The experimental results show that the Hedera algorithm proposed in this article is fair, the throughput reaches 13986.3 TPS, and the resource consumption is much lower than the PoW consensus. By analyzing its security and liveness, it can resist the sybil attack, nothing-at-stake attack, selfish mining attack, and message hijacking attack, and has good liveness.
Ballots security and reliability is two of the crucial things that makes digital based ballots system not widely implemented. Paper-based ballots have the downsides of environmental impact on making paper. Blockchain has one of the advantages on the security aspect where the blocks are secured using cryptographic protocol that makes it hard to be hacked. This paper will be examining the possibility of ballots implementation using a microcontroller-based ballots system. The microcontroller is used because it is cheap to implement and it has the capability to sign the blockchain transaction. This paper is a proof of concept for a smaller scale ballots system for a small organization with two candidate option. The blockchain network used in this paper is a Ethereum test network provided by Ropsten. The number of testing done is 60 times total for the two candidate. The smaller scale testing proven to be successful with the transaction confirmed successful in etherscan.io.
Blockchain technology has received a lot of attention recently due to its potential to create decentralized and secure data systems. This technology is a digital ledger technology that is tamper-evident and tamper-resistant, offering a new way of storing and sharing data. In this review, the author will summarize the present state of research on blockchain technology, including its applications and benefits, as well as potential pitfalls. The author will review the latest research on blockchain technology in different domain areas, comparatively finance, healthcare, and supply chain management, and provide insights into the future direction of this technology. The main functions of security deposits, such as the financial management system, computerized order entry system, and information interchange, have received a lot of attention in many pieces of literature. In this research paper, the author reviews the existing research papers and applications available for the security deposit using blockchain technology. The goal of this research paper is to give readers a thorough grasp of the state of blockchain technology research today and how it might affect a variety of fields.
Krishna Prakash R, N Sarmiladevi, R M Balajee, A Indhuia
Hash link is exploited for digital images to obtain imagecord concepts derived from cryptographic formation. The primary difference from the Blockchain imagecord concept is that the data block will not be kept as a collection of individual blocks but rather will be joined by an embedding method. Accordingly, standard image files are used in the same way as it was created before. An extra data block is also cultivated in it to preserve the imagecord from its origin point. The proposed method requires no supplementary data apart from the original image. This solution is also suitable for multiple graphics file formats, making it a portable version with platform independence. Simultaneously, this method also gives higher security against the forgery of the original file without losing its properties. This was attained by embedding the hashing data with the image’s data block without losing its integrity by preventing alternation. This paper deals with the concept of Blockchain distributed ledger to apply in imagecord by embedding algorithms and data blocks in the image.
The metaverse is a unified, persistent, and shared multi-user virtual environment with a fully immersive, hyper-temporal, and diverse interconnected network. When combined with healthcare, it can effectively improve medical services and has great potential for development in realizing medical training, enhanced teaching, and remote surgical treatment. The metaverse provides immersive services for users through massive and multimodal data, and its data scale and data growth rate are bound to show exponential growth. Blockchain-based distributed storage is a fundamental way to keep the metaverse running continuously; however, many blockchains, such as Ethereum and Filecoin, suffer from low transaction throughput and high latency, which seriously affect the efficiency of distributed storage services and make it difficult to apply them to the metaverse environment. To this end, this paper first proposes a network architecture for distributed storage systems based on proof of retrievability to address the problem of centralized decision making and single point of access in centralized storage. The secure data storage of the metaverse health system is ensured. Secondly, we designed two data transmission protocols through vector commitment and encoding functions to achieve the transfer of time cost from the critical path to storage nodes and improve the efficiency of data verification between nodes as well as the scalability of the metaverse health system. Finally, this paper also conducts security analysis and performance analysis of the proposed scheme, and the results show that our scheme is secure and efficient.
The deep integration of Internet of Medical Things (IoMT) and Artificial intelligence makes the further development of intelligent medical services possible, but privacy leakage and data security problems hinder its wide application. Although the combination of IoMT and federated learning (FL) can achieve no direct access to the original data of participants, FL still can't resist inference attacks against model parameters and the single point of failure of the central server. In addition, malicious clients can disguise as benign participants to launch poisoning attacks, which seriously compromises the accuracy of the global model. In this paper, we design a new privacy protection framework (BFG) for decentralized FL using blockchain, differential privacy and Generative Adversarial Network. The framework can effectively avoid a single point of failure and resist inference attacks. In particular, it can limit the success rate of poisoning attacks to less than 26%. Moreover, the framework alleviates the storage pressure of the blockchain, achieves a balance between privacy budget and global model accuracy, and can effectively resist the negative impact of node withdrawal. Simulation experiments on image datasets show that the BFG framework has a better combined performance in terms of accuracy, robustness and privacy preservation.