Blockchain has attracted widespread attention due to its unique features such as decentralization, traceability, and tamper resistance. With the rapid development of blockchain technology, an increasing number of industries are gradually applying blockchain technology to various fields such as the Internet of Things, healthcare, finance, agriculture, and government affairs. However, there are certain differences in the underlying architecture, data structures, consensus algorithms, and other aspects of blockchain technology across different sectors, which restrict transactions to occur within a single blockchain. Achieving interoperability between different blockchains is challenging, hindering data exchange and collaborative business to some extent, inevitably leading to the problem of "data silo". Against this backdrop, this study aims to explore a cross-chain solution based on relay technology to address the current challenges of interoperability between blockchain systems. By employing relay-based cross-chain technology, a blockchain cross-chain collaboration platform is established to simulate the construction of a real cross-chain network. By deploying business contracts, data and resources between heterogeneous blockchains can seamlessly communicate, resolving the challenge of cross-chain interoperability. The research findings demonstrate that the blockchain cross-chain solution based on relay technology can effectively enhance interoperability between different blockchain systems, enabling cross-chain asset circulation and information transmission, highlighting the practical applicability and scalability of this study.
In this chapter, we embark on a journey through the dynamic intersection of blockchain technology and federated machine learning (FML). This chapter elucidates the pivotal role of FML in mitigating data privacy concerns in the ever-expanding field of Artificial Intelligence. This chapter begins by unveiling the intrinsic challenges stemming from centralized data collection and traditional machine learning (ML) methods. It emphasizes the urgent need for innovative solutions that not only enhance learning efficiency but also ensure the confidentiality and security of sensitive data. FML emerges as a promising paradigm where disparate parties collaboratively train ML models without the necessity of centralized data aggregation. This chapter underscores the merits of FML, which encompass data privacy preservation and efficient model training. However, it doesn&s;t shy away from exposing the stumbling blocks encountered, including centralization issues, potential adversarial updates, and the overarching data privacy dilemma. To address these challenges, this chapter introduces blockchain technology as a robust foundation. Ethereum and smart contracts (SCs) take center stage as they empower a decentralized framework for federated learning. An SC assumes the role of a coordinator, ensuring the secure aggregation of model updates. Furthermore, the innovative application of zero-knowledge STARK proofs is employed to verify the legitimacy of client training processes, thus upholding the integrity of data. This chapter concludes by evaluating the performance of this blockchain-facilitated federated learning model, showcasing promising results that are in close proximity to theoretical limits. However, it also highlights trade-offs, including increased time requirements and gas costs associated with blockchain integration. In sum, this chapter sets the stage for an exciting exploration of how blockchain technology can empower federated learning, transforming it into a robust, secure, and efficient approach to ML while safeguarding data privacy. It opens the door to further research and innovation in this rapidly evolving and highly relevant field.
Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless networks. However, edge computing servers (ECSs) from different operators may not trust each other, and thus the incentives for collaboration cannot be guaranteed. In this paper, we propose a consortium blockchain enabled collaborative edge computing framework, where users can offload computing tasks to ECSs from different operators. To minimize the total delay of users, we formulate a joint task offloading and resource optimization problem, under the constraint of the computing capability of each ECS. We apply the Tammer decomposition method and heuristic optimization algorithms to obtain the optimal solution. Finally, we propose a reputation based node selection approach to facilitate the consensus process, and also consider a completion time based primary node selection to avoid monopolization of certain edge node and enhance the security of the blockchain. Simulation results validate the effectiveness of the proposed algorithm, and the total delay can be reduced by up to 40% compared with the non-cooperative case.
The healthcare industry has advanced its digitalization and use of electronic medical records during the past ten years (EMRs). The EHR system gives the information's proprietor authority over their data and allows them to communicate it to certain individuals. It is challenging for data to maintain security and diagnostic processes because of the enormous volume of data in the medical field. This research presents a novel blockchain-based encryption system using deep learning (BcEs-DLM) for secure medical data management. The concept that is being described encompasses many phases of activities, including safe data management via blockchain, encryption, and optimal key generation. It provides individuals with the ability to manage data accessibility, granting read/write access to hospital authorities, and triggering precautionary agreements. Our recommended approach offers a reliable methodology for generating secure encryption keys and effectively safeguarding sensitive medical data using the block cipher technique. By following this method, you can ensure that patient information remains confidential and protected from unauthorized access. The detection process is performed using medical record sharing. In this paper, we achieved a 97 percent accuracy after training our deep learning model. Additionally, every node of this system is registered and updated on the blockchain
Ateeq Ur Rehman, Nargis Tariq, Mian Ahmad Jan, Fazlullah Khan · 6 authors
In recent years, the healthcare industry has undergone a digital transformation, making patient data publicly available and accessible. Healthcare units make a portion of the data public while keeping the rest private, necessitating various mechanisms for security and privacy. Blockchain technology has been widely adopted in the healthcare sector to secure data transactions. However, public blockchains face challenges in scalability and privacy, whereas private blockchains struggle with centralization, interoperability, and complexity. To address these challenges, we propose an Internet of Medical Things (IoMT)-based hybrid blockchain architecture. The proposed architecture combines the decentralized Ethereum and the centralized Hyperledger Fabric blockchain (Eth-Fab) using SQLite to leverage Ethereum smart contracts with the Hyperledger permission model. Moreover, we introduce access control strategies to enhance patient data authentication and authorization. We have employed machine learning algorithms to assist healthcare practitioners in accurately detecting diseases and making time-efficient decisions. Additionally, we modeled the proposed architecture using the M/M/1 queuing model and derived closed-form expressions for latency, throughput, and server utilization. The validity of these expressions was verified through Monte Carlo simulations. The results demonstrate that higher service times (block generation) yield better outcomes in terms of latency, throughput, and utilization, regardless of the arrival time, i.e., transactions in the mining pool.
Xianghe Ye, Qingxu Di, Xinyuan Du, Ayong Bao · 5 authors
In this paper, we present an innovative solution to overcome the limitations of blockchain systems, particularly in terms of communication costs and storage resources. Our ap-proach leverages blockchain sharding, a technique that partitions a blockchain into multiple parallel shards. We underscore the im-portance of selecting a sharding algorithm that accounts for the temporal dimension, often overlooked in prior studies. This algorithm is designed to minimize inter-shard communication and en-sure a well-balanced distribution of nodes. Additionally, we intro-duce a novel methodology that integrates the gated recurrent unit (GRU) and graph neural network (GNN) for the sharding algorithm, incorporating time-related information. This involves a graph extractor for extracting temporal graph data from block-chain data and a Dual-GRU-GNN model for learning from these temporal graphs to enhance prediction results. Our proposed so-lution is specifically crafted to handle dynamic Ethereum-like blockchain networks that evolve over time, aiming to yield supe-rior results in the realm of blockchain sharding. Compared to clas-sical neural network models such as Graph Neural Network (GCN), Graph Sage, and Graph Attention Network (GAT), our proposed model exhibits superior overall performance. It achieves a more evenly distributed number of nodes within each shard and establishes stronger correlations among nodes within the same shard, minimizing the need for cross-shard communication.
The exponential growth of the Internet of Things (IoT) alongside the increasing significance of cryptocurrencies has unveiled critical security challenges in digital transactions. This study uses a comprehensive method to design, create, and assess secure and efficient cryptocurrency wallets specifically designed for the Internet of Things (IoT) environment. The study presents the Enhanced Elliptic Curve Digital Signature Algorithm (EECDSA), which integrates sophisticated ECDSA, blockchain technology, Golang programming language, and JSON data exchange. The design methodically emphasizes scalability, interoperability, and strict adherence to security protocols for various IoT devices and networks. The study thoroughly analyzes ECDSA and introduces EECDSA, highlighting the double-and-add algorithm to enhance efficiency. Comparative analyses show that EECDSA outperforms RSA, ECDSA, and multi-signature algorithms in terms of execution time and memory usage on different CPUs, particularly on ARM-based architectures. The results highlight EECDSA’s capacity for efficient cryptographic operations, making it suitable for IoT devices with constrained resources.
This survey paper provides a comprehensive and in-depth overview of blockchain technology and its wide-ranging applications. It begins by introducing the fundamental characteristics and structure of blockchain, with a particular focus on the five major consensus mechanisms and their unique features. The article emphasizes the crucial role of smart contracts and cryptography in the construction and operation of blockchain networks. Furthermore, the paper explores the specific applications of blockchain in three key areas: cryptocurrencies, supply chains, and healthcare security. It highlights the numerous advantages that blockchain brings to these domains, including enhanced security, transparency, and efficiency. The paper also offers valuable insights into the future potential of blockchain technology in these areas, providing a glimpse into the possibilities that lie ahead. Additionally, the article addresses the challenges posed by the "impossible triangle" of decentralization, security, and high performance in blockchain. It discusses the emerging research trends aimed at tackling these challenges, such as cross-chain protocols, privacy protection mechanisms, blockchain expansion strategies, and advanced data storage solutions. The paper presents recent advancements and breakthroughs in each of these research directions, showcasing the ongoing efforts to overcome the limitations of blockchain technology.
The convergence of blockchain, Metaverse, and non-fungible tokens (NFTs) brings transformative digital opportunities alongside challenges like privacy and resource management. Addressing these, we focus on optimizing user connectivity and resource allocation in an NFT-centric and blockchain-enabled Metaverse in this paper. Through user work-offloading, we optimize data tasks, user connection parameters, and server computing frequency division. In the resource allocation phase, we optimize communication-computation resource distributions, including bandwidth, transmit power, and computing frequency. We introduce the trust-cost ratio (TCR), a pivotal measure combining trust scores from users’ resources and server history with delay and energy costs. This balance ensures sustained user engagement and trust. The DASHF algorithm, central to our approach, encapsulates the Dinkelbach algorithm, alternating optimization, semidefinite relaxation (SDR), the Hungarian method, and a novel fractional programming technique from a recent IEEE JSAC paper [2]. The most challenging part of DASHF is to rewrite an optimization problem as Quadratically Constrained Quadratic Programming (QCQP) via carefully designed transformations, in order to be solved by SDR and the Hungarian algorithm. Extensive simulations validate the DASHF algorithm’s efficacy, revealing critical insights for enhancing blockchain-Metaverse applications, especially with NFTs.
Saba Inam, Shamsa Kanwal, Rabia Firdous, Fahima Hajjej
Improved software for processing medical images has inspired tremendous interest in modern medicine in recent years. Modern healthcare equipment generates huge amounts of data, such as scanned medical images and computerized patient information, which must be secured for future use. Diversity in the healthcare industry, namely in the form of medical data, is one of the largest challenges for researchers. Cloud environment and the Block chain technology have both demonstrated their own use. The purpose of this study is to combine both technologies for safe and secure transaction. Storing or sending medical data through public clouds exposes information into potential eavesdropping, data breaches and unauthorized access. Encrypting data before transmission is crucial to mitigate these security risks. As a result, a Blockchain based Chaotic Arnold's cat map Encryption Scheme (BCAES) is proposed in this paper. The BCAES first encrypts the image using Arnold's cat map encryption scheme and then sends the encrypted image into Cloud Server and stores the signed document of plain image into blockchain. As blockchain is often considered more secure due to its distributed nature and consensus mechanism, data receiver will ensure data integrity and authenticity of image after decryption using signed document stored into the blockchain. Various analysis techniques have been used to examine the proposed scheme. The results of analysis like key sensitivity analysis, key space analysis, Information Entropy, histogram correlation of adjacent pixels, Number of Pixel Change Rate, Peak Signal Noise Ratio, Unified Average Changing Intensity, and similarity analysis like Mean Square Error, and Structural Similarity Index Measure illustrated that our proposed scheme is an efficient encryption scheme as compared to some recent literature. Our current achievements surpass all previous endeavors, setting a new standard of excellence.
Open access
Brain Tumor Detection and Classification
Chaos-based Image/Signal Encryption
Advanced Steganography and Watermarking Techniques
Internet of Things (IoT) is becoming increasingly significant and has the ability to benefit society. It offers several benefits such as interconnectivity, ubiquity, data generation, automation, real time monitoring, predictive analysis and resource optimization. It can be used in a variety of settings, such as buildings, businesses, healthcare, agriculture, and transportation, among others. Its widespread use makes it a flexible and potent technology. Due to its potential to revolutionize healthcare delivery, improve patient outcomes, increase operational efficiency, and lower costs, the IOT is crucial for the healthcare industry. While IoT-based healthcare solutions have numerous advantages, there are also a number of problems and difficulties that must be properly resolved in order for them to be successfully implemented and used. The primary concern is security and privacy. Integration of blockchain and IOT can be highly useful in various healthcare industry by addressing critical issues related to security, trust, data integrity, and automation. The primary objective of this study is to demonstrate how IOT based healthcare sensors can communicate with blockchain networks to make the whole healthcare eco-system highly secure and efficient. Initially, effective blockchain framework and RPM (Remote Patient Monitoring) sensors were identified. This study deployed Hyperledger fabric framework to implement the RPM system. Thereafter, two experiments were conducted and it is found that there was no packet loss when RPM transactions were sent to blockchain network at the speed of 2 transactions per minutes but the system encounter with packet loss at 5 transactions per minute.
The technology of blockchain is popular because of its properties like transparency, immutability, control decentralization, and distributed storage. The skill verification and student certificate are utilized for the application of job and other purposes. The certificate forgery with centralized authority is a primary problem in online education. This problem is addressed employing blockchain-based management of certificate for students and the management of skill evidence for employees. However, those kinds of management of certificate have privacy and security problems. To address these problems, the Elliptical Curve Digital Signature Algorithm (ECDSA)-based Proof-of-Stake (POS) is proposed for educational resource sharing on certificate management to solve privacy and security issue in blockchain. In the signature verification stage, the POS employed to verify the transaction of a blockchain and establish new blocks in a blockchain. The proposed ECDSA-POS technique achieves better file upload time of 0.0002541 seconds in 50 kb and single transaction time of 45 milli seconds compared to existing method like PHP blockchain.
Amir Meydani, Hossein Shahinzadeh, Ali Ramezani, Majid Moazzami · 6 authors
In recent times, there has been a growing fascination in the blockchain technology (BC), leading to the development of many applications leveraging its attributes such as decentralization, transparency, fault tolerance, and robust security. In the domain of smart grids (SGs), numerous suggestions have arisen regarding the incorporation of BC to enhance various aspects such as intelligent energy management, energy trading, security and privacy preservation, microgrid (MG) administration, and eletric vehicles (EVs). This article conducts a comprehensive analysis of more than seventy scholarly works, projects, and testbeds to investigate seven key fields in the electrical industry where BC could potentially yield substantial benefits. Finally, an analysis of the technical obstacles associated with this technology will be conducted, followed by an exploration of the potential areas for future research.
The development of Sixth Generation (6G) communication technology plays a pivotal role in emerging intelligent healthcare applications. However, the vast amount of data not only occupies channel resources but also poses challenges to the timeliness and security of remote medical treatment. This paper constructs a remote intelligent healthcare system based on blockchain, aiming to achieve the minimum Age of Information (AoI) and energy consumption of medical data transmission while ensuring the security of intelligent healthcare data. Our system contains two layers: the Sensing Communication Layer (SenCom-Layer) and the Blockchain Layer (BlockC-Layer). In SenCom-Layer, we propose an Energy-aware Whittle Index-based Algorithm (EWIA), which obtains the optimal scheduling strategy for medical data by solving a relaxed Restless Combinatorial Multi-Armed Bandit (RCMAB) problem. In BlockC-Layer, considering both the dual utility of block nodes and the system, we introduce an Energy-efficient DPoS-based cooperative game, which achieves Nash equilibrium through Nash bargaining among the two utilities. Finally, the effectiveness of the proposed algorithm is demonstrated through experiments in terms of security and energy consumption.
Blockchain technology has revolutionized various industries, offering decentralized and tamper-resistant data storage and transaction capabilities. However, traditional consensus mechanisms, such as proof-of-work (PoW) and proof-of-stake (PoS), face energy consumption, scalability, and security challenges. This chapter proposes a novel consensus mechanism called “BIONET,” a bio-inspired neural network for blockchain systems. BIONET integrates the principles of swarm intelligence and artificial neural networks to achieve efficient, secure, and adaptive consensus in blockchain networks. The authors present the architectural overview of BIONET, highlighting its adaptability and self-organization capabilities. Furthermore, they demonstrate BIONET's effectiveness in PoW, PoS, and practical byzantine fault tolerance (PBFT) consensus mechanisms. Finally, they discuss the future directions and challenges of BIONET, paving the way for bio-inspired optimization techniques in blockchain systems.
Recent years, the successful application of blockchain in cryptocurrency has attracted a lot of attention, but it has also led to a rapid growth of illegal and criminal activities. Phishing scams have become the most serious type of crime in Ethereum. Some existing methods for phishing scams detection have limitations, such as high complexity, poor scalability, and high latency. In this article, we propose a novel framework named phishing detection on Ethereum via augmentation ego-graph based on graph neural network (PEAE-GNN). First, we obtain account labels and transaction records from authoritative websites and extract ego-graphs centered on labeled accounts. Then we propose a feature augmentation strategy based on structure features, transaction features and interaction intensity to augment the node features, so that these features of each ego-graph can be learned. Finally, we present a new graph-level representation, sorting the updated node features in descending order and then taking the mean value of the top n to obtain the graph representation, which can retain key information and reduce the introduction of noise. Extensive experimental results show that PEAE-GNN achieves the best performance on phishing detection tasks. At the same time, our framework has the advantages of lower complexity, better scalability, and higher efficiency, which detects phishing accounts at early stage.
Neurological disorders are a significant health challenge globally, affecting millions of individuals and imposing a considerable economic burden on healthcare systems. Early and accurate diagnosis plays a crucial role in improving patient outcomes and managing these disorders effectively. This abstract presents a novel approach that combines blockchain technology with deep learning algorithms to enhance the detection of neurological disorders. The proposed system leverages the decentralized and transparent nature of blockchain to securely store and share medical data, enabling seamless collaboration among healthcare providers, researchers, and patients. This infrastructure ensures data integrity, privacy, and accessibility, addressing critical concerns in medical data management. Furthermore, the deep learning approach employs advanced neural network architectures, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to analyze large-scale neurological data, including medical images, electroencephalograms (EEGs), and clinical records. By leveraging the power of deep learning, the system can automatically extract relevant features and patterns from complex neurological data, enabling accurate diagnosis and early detection of various disorders. The integration of blockchain and deep learning offers several advantages. Firstly, it facilitates secure and decentralized storage of medical data, ensuring patient privacy and data integrity. Secondly, it enables seamless data sharing and collaboration among multiple stakeholders, promoting knowledge exchange and enhancing research capabilities. Lastly, deep learning algorithms improve the accuracy and efficiency of neurological disorder detection, enabling timely interventions and personalized treatment plans. The proposed system holds great potential in revolutionizing the field of neurological disorder diagnosis and management. By leveraging the combined power of blockchain and deep learning, healthcare providers can enhance their diagnostic capabilities, leading to improved patient outcomes, reduced healthcare costs, and accelerated research advancements. However, further research and development are necessary to address technical challenges, scalability issues, and regulatory considerations to realize the full potential of this innovative approach.
Eranga Bandara, Peter Foytik, Sachin Shetty, Ravi Mukkamala · 8 authors
The main challenges in the 5G/6G network slicing are resource sharing, network slice orchestration, and network optimization in the 5G ecosystem. This paper proposes a novel architecture for a dynamic network slice broker and marketplace named “SliceGPT” that leverages Custom-Trained OpenAI GPT-3.5 LLM, blockchain and NFTs to enable collaboration between different stakeholders in the 5G ecosystem to address these challenges. The platform enables different stakeholders in 5G network slicing (e.g., cloud providers, network operators, RAN providers, and transport network providers) to share and rent their resources to create customized network slices that meet the specific requirements of 5G applications. The orchestration of network slices is managed through blockchain smart contracts, and the resulting network slices are encoded as NFT tokens and made available for purchase in a decentralized NFT marketplace. Customers can select and purchase the network slices that best meet their needs by paying either crypto or flat currency. Revenue generated through the sale of network slices is distributed among different providers, facilitating a fair and efficient marketplace. Intelligent network slice optimization is accomplished through the utilization of a custom-trained GPT-3.5 LLM(which powers the ChatGPT). This LLM can generate valuable insights and recommendations from extensive network datasets, contributing to the optimization of network slices for enhanced performance and efficiency. A prototype of SliceGPT has been implemented with FreedomFi 5G gateway, OpenAirInterface 5G core, OpenAI GPT-3.5-turbo model, LlamaIndex and Langchain. To the best of our knowledge, this is the very first research endeavor to incorporate the GPT LLMs for optimizing 5G/6G network slicing,
G. Sreenath, Gowri T Sridhar, Aditya A. Sannabhadti, Robin Mercy S J · 5 authors
Digital identity is crucial to the growth and profitability of the digital economy, as well as the growth and profitability of every organization and industry. Identity management software is typically meant to assist you in managing digital identities. In real-world applications, authentication and other activities are frequently employed. In recent years, attempts have been made to create block-chain-based identity management solutions that allow users to take control of their identity. Blockchain-based solutions have the potential to increase the efficiency of public and private sector government operations and service delivery. Blockchain has the potential to decrease expenses and fraud while improving customer service. Blockchain data is cryptographically linked and dispersed across several computers, making manipulation practically difficult. Block-chain technology can pave the way for digital identities in decentralized networks while preserving privacy, trust, and security, and it is the best and most secure platform for developing digital identity. However, the current identity management system is neither secure nor dependable. To solve the current limitations/difficulties, the Ethereum block-chain is utilized in this project to securely store identity data and share the approved identity data with applications, making it safer for users, removing the difficulty in verification, and making it fraud-proof.
In electronic healthcare, patient medical imaging data is critical for remote diagnostic procedures. The increasing demand to harness the potential of these medical images necessitates their secure sharing among various entities, including hospitals, medical institutions, and insurance companies. However, third-party access and possible manipulation make it challenging to maintain the ownership and integrity of this data. This study introduces a novel approach that combines compression, digital watermarking, symmetric encryption, and blockchain technology to protect medical images from unauthorized third-party interventions. Using the Discrete Wavelet Transform, our proposed technique embeds a compressed watermark into the host image. Specifically, the watermark is encoded into vectors and inserted into the second-level approximation, i.e., the Low-Low of the image using the Least Significant Bit, producing a watermarked image. The watermarked data is encrypted and stored on a blockchain to further safeguard these images’ integrity. This multi-layer security framework not only preserves the integrity and confidentiality of the data but also facilitates transparent and secure sharing among stakeholders. The proposed method achieves a peak signal-to-noise ratio of 63.24dB and a structural similarity index of 1. These results demonstrate the robustness of our solution in protecting and exchanging medical images within the digital healthcare ecosystem, positioning it as an advanced and reliable option for secure data management.
Open access
Advanced Steganography and Watermarking Techniques