Telesurgery is transforming healthcare by enabling surgeons to perform operations remotely through robotic systems connected to high-speed networks. The reliability and safety of these procedures depend on seamless communication, often secured using Proof-of-Stake (PoS) blockchain technology to ensure data integrity and validate transactions. However, malicious nodes within PoS blockchain networks pose significant risks by introducing delays, invalidating legitimate transactions, or colluding to compromise the system. This paper proposes a Deep Learning (DL) based framework to detect malicious nodes in PoS-based blockchain applications, ensuring secure and reliable operations. Using a dataset of node activity, DL models—LSTM, 1D-CNN, and FFNN—were trained with optimizers including Adam, Nadam, and RMSprop. Among these, the LSTM model with RMSprop achieved the highest detection accuracy of 87.37%. The framework enhances security by enabling real-time malicious node detection and communication monitoring, addressing key challenges in blockchain integrity and operational precision, ultimately ensuring the security and reliability of blockchain-integrated telesurgical systems.
As digital evidence increasingly growing in significance in healthcare forensics, safeguarding sensitive medical data's confidentiality, integrity, and limited access remains to be an important issue. Existing forensic evidence management systems are subject to data breaches and illegal access since they frequently lack significant privacy-preserving measures. In order to overcome such challenges, this research suggests a Blockchain-Based Custody Evidence Management System for Healthcare Forensics, which combines blockchain technology, machine learning, and encryption methods to improve security, privacy, and accessibility. To ensure accurate and efficient gathering of information, machine learning algorithms are used to extract handwritten and printed text from medical photographs. AES encryption ensures safe storage, while Fully Homomorphic Encryption (FHE) is used for dynamic access level control to protect gathered evidence. Identity verification is made possible via a web-based authentication system that uses Zero-Knowledge Proofs (ZKP) to protect privacy by preventing the disclosure of personal data. By preventing unintended modifications, blockchain technology is used to preserve the custody chain's integrity. Furthermore, machine learning-driven PII detection and masking methods balance the requirement for forensic investigation with privacy compliance by controlling data accessibility according to access entitlements. Based on permitted access levels, the system makes it possible to share safe evidence with law enforcement agencies, such as courts, the police, and other forensic groups. Using blockchain to guarantee data immutability, cryptographic security to restrict access, and artificial intelligence (AI) to safeguard data, this approach enhances the privacy, security, and dependability of handling forensic evidence in medical investigations.
Blockchain Technology Applications and Security
Artificial Intelligence in Healthcare and Education
Federated learning (FL) emerges as a distributed training method in the Internet of Vehicles (IoVs), which promotes connected and automated vehicles (CAVs) to train a global model by exchanging models instead of raw data to protect data privacy. In this paper, consider the limitation of model accuracy and communication overhead in FL, as well as further verification in the real scenarios, we propose a directed acyclic graph (DAG) blockchain-based IoV system that comprises a DAG layer and a CAV layer for model sharing and training, respectively. Furthermore, a DAG blockchain-assisted asynchronous federated mutual learning (DAFML) algorithm is introduced to improve the model accuracy, which utilizes mutual distillation method to train a teacher-student model simultaneously. Moreover, a policy network will first be pre-trained by an expert data augmentation strategy through the DAFML algorithm via the behavior cloning, and be re-trained through the proposed proximal policy optimization (PPO) algorithm based autonomous driving framework. Finally, simulation results demonstrate that the proposed DAFML algorithm outperforms other benchmarks in terms of the model accuracy, distillation ratio and autonomous driving decision.
The secure and efficient storage and sharing of medical images have become increasingly important due to rising security threats and performance limitations in existing healthcare systems. Centralized systems struggle to provide adequate privacy, rapid access, and reliable storage for sensitive medical images. This paper proposes a decentralized medical image-sharing framework to address these issues by integrating blockchain technology, the InterPlanetary File System (IPFS), and edge computing. Blockchain technology enforces secure patient-centric access control through smart contracts that enable patients to directly manage their data-sharing permissions. The IPFS provides decentralized and scalable storage for medical images and effectively resolves the storage limitations associated with blockchain. Edge computing enhances system responsiveness by significantly reducing latency through local data processing to ensure timely medical image access. Robust security is ensured by using elliptic curve cryptography (ECC) for secure key management and the Advanced Encryption Standard (AES) for encrypting medical images to protect against unauthorized access and data breaches. Additionally, the system includes real-time monitoring to promptly detect and respond to unauthorized access attempts to ensure continuous protection against potential security threats. System results demonstrate that the proposed framework achieves lower latency, higher throughput, and improved security compared to traditional centralized storage solutions, which makes our system suitable for practical deployment in modern healthcare settings.
Hao-Tse Chung, Shao‐Hung Cheng, Yu‐Jia Chen, Li‐Chun Wang
Emerging Blockchain-empowered Federated Learning (BCFL) technology combines the decentralized security of blockchain with the privacy protection of federated learning. BCFL addresses the issue of single points of failure in centralized systems, making it an increasingly popular solution. However, current consensus mechanisms, such as Proof of Work (PoW), Proof of Stake (PoS), and Practical Byzantine Fault Tolerance (PBFT), lead to challenges such as high computational costs and limited scalability. This paper proposes a Batch Zero-Knowledge Proof-based practical Byzantine fault-tolerant (BZ-BFT) consensus mechanism for BCFL to enhance efficiency and reliability. By integrating Zero-Knowledge Proof (ZKP), our approach enables the verification of the primary node's proposal without revealing information from other network nodes, thereby ensuring the credibility of the aggregated results. To address the high computational overhead associated with ZKP, we present a batch quantization preprocessing technique called BatchZKP. Our proposed BZ-BFT reduces initialization, proof generation, and verification time by$97.81 \%, 70.0 \%$, and 47.64 %, respectively, significantly boosting BCFL system efficiency and reliability. Additionally, our approach reduces communication complexity from$O\left(n^{2}\right)$to$O(n)$and enhances Byzantine fault tolerance to${1/2}$.
Smart contracts were introduced as autonomous programs running across a blockchain network. To solve the difficulty that smart contracts on the blockchain cannot interact with the real world, some blockchain oracle implementation schemes have been proposed. However, the existing data feed schemes still cannot meet the demand of off-chain intensive data feed. This paper introduces a directed acyclic graph (DAG)-distributed ledger into the blockchain oracle data feed scheme, to propose a DAG-distributed ledger-based decentralized oracle network (DDON) framework. The lightweight DAG consensus mechanism ensures data integrity and significantly reduces the entry of valueless information into the DDON, ultimately generating deterministic data. The proposed DAG structure enables parallel processing of transactions and accelerates the efficiency of data feeds. This also enables the feeding of historical data. In addition, an off-chain data feed mechanism is designed for off-chain intensive streaming of data feeds through the proposed oracle network, to separate data feeds from data fetches and improve the efficiency of feeding multiple requests. The evaluation results and discussions demonstrate that the proposed framework reduces the response time for every smart contract request and is more efficient and flexible than other mainstream oracle data feed services.
Aiming at the severe challenges of Intelligent Connected Vehicle (ICV) in the field of data security, this paper designs a four-layer ICV data security framework, which covers the whole process from data collection, processing, storage and sharing to privacy protection. In the security framework, three technical methods of blockchain, Zero-Knowledge Proof (ZKP) and Post-Quantum Cryptography (PQC) are integrated. Blockchain is used to provide distributed trust mechanism and tamper-resistant data storage. ZKP is used to protect data privacy and realize data verification without leaking sensitive information. PQC is used to enhance data encryption and authentication mechanism to resist quantum computing attacks. At the same time, the data sharing security strategy is formulated, including initialization and key generation, data transmission preparation, zero-knowledge proof generation and verification, data reception and verification, blockchain recording and consensus, access control and data usage, etc., to ensure the security and privacy protection of data during the sharing process. The simulation experiment data of delay time, throughput and privacy protection intensity in the simulation environment show that the scheme not only enhances data privacy protection and data integrity verification but also improves the system's ability to resist future quantum computing threats and provides a solid guarantee for the data security of intelligent connected vehicles.
Md. Sameeruddin Khan, Tom Chen, Mithileysh Sathiyanarayanan, Mohammed Mujeerulla · 5 authors
The Internet of Things (IoT) model is presented in this paper with multi-layer security based on the Lenstra-Lenstra-Lovasz (LLL) algorithm. End nodes for the Internet of Things include inexpensive gadgets like the Raspberry Pi and Arduino boards. It is not practical to run rigorous algorithms on them, as opposed to computer systems. Therefore, a cryptography procedure is required that could function on this IOT equipment. Bitcoins and Ethereum are examples of cryptocurrency and Ripple employs techniques such as elliptic curve digital signature, Elliptic-Curve Diffie-Hellman (ECDH), and algorithm to sign any cryptocurrency on SECP256k1 elliptic curves transactions. By using Lenstra-Lenstra-Lovasz on a real-world Bitcoin blockchain and applying it to multiple dimensions, such as nonce leakage and weak nonces across several elliptic curves with different bit sizes on a Raspberry Pi, we can demonstrate the security of elliptic curve cryptosystems. Public key encryption techniques are seriously threatened by the development of quantum computing. Therefore, employing lattice encryption with Nth Degree Truncated Polynomial Ring Units (NTRU-NTH) on the Bitcoin blockchain will increase the resistance of Bitcoin blocks to quantum computing assaults. The execution time taken on SECP256k1 is 131.7 Milli seconds comparatively faster than NIST-224P and NIST-384P.
The goal of this current study is to address important concerns about data security, privacy, and integrity by amalgamating blockchain technology with the Internet of Medical Things. The IoMT ecosystem consists of wearables, implanted sensors, and remote monitoring tools that generate sensitive medical data continuously, revealing several security vulnerabilities. Blockchain, with its principles of decentralization, transparency, immutability, and cryptographic security, opens up new avenues for securing health data without the use of third-party authorities. This paper outlines the methodology used in this review, including a systematic analysis of relevant literature, utilizing the PRISMA framework to evaluate sources. The analysis identifies key protocols and components of blockchain relevant to IoMT, highlights challenges, and provides solutions. Key findings emphasize blockchain’s ability to reduce attacks using distributed ledgers, permissioned access, and encrypted transactions. Furthermore, blockchain may improve patient care by providing real-time data exchange and enabling interoperability across health systems.
M. Baritha Begum, B. Suganthi, P. Sivagamasundhari, S. A. Arunmozhi · 5 authors
ABSTRACT Mobile ad hoc networks (MANETs) integrated with the Internet of Things (IoT) form a decentralized communication framework crucial for 6G environments. However, ensuring secure and efficient routing in such networks remains a challenge due to their distributed nature and vulnerability to attacks. This paper introduces a heterogeneous local directed acyclic graph blockchain (HLDAG‐BC) combined with recalling enhanced recurrent neural networks (RERNNs) for secure and efficient routing in MANET‐IoT environments. The HLDAG‐BC offers tamper‐proof communication and identity‐based conditional privacy‐preserving authentication (ICPA) is a lightweight and secure node authentication scheme. Network nodes are grouped using the kernel neutrosophic c‐means (KNCM) algorithm. The optimal cluster heads are chosen using the red piranha optimization (RPO) method. RERNN determines the shortest routing path to increase reliability and minimize latency. Furthermore, an HDLNN is used for intrusion detection to achieve robust network security. The HLDAG‐BC‐RERNN approach proposed shows that the packet delivery ratio improves by 31.35%, throughput by 34.56%, latency by 30.29%, and network lifetime by 28.67% compared to the existing approaches, as shown in the comprehensive evaluations. In conclusion, the proposed framework offers a scalable and secure solution for MANET‐IoT networks, making it a viable approach for future 6G applications.
Roger T. Tomihama, M. C. Wilkinson, Sharon C. Kiang
Blockchain technology (BCT) enables the building of a distributed decentralized network that securely stores and exchanges unchangeable data, controlled by individual users. In health care, BCT may help streamline interoperability and information transmission while guaranteeing medical record authenticity and safeguarding patient privacy. Possible applications in radiology include patient-controlled image sharing, facilitation of multiinstitutional research, and artificial intelligence integration. Radiologists should stay informed of BCT given its ongoing improvements and unique potential to support the specialty's needs.
Open access
Artificial Intelligence in Healthcare and Education
The growing number of consumer Internet of Things (IoT) gadgets, including smart homes, fitness trackers, connected appliances, and home security systems, is transforming the way we live our daily lives. This has led to the emergence of a collaborative cloud-edge paradigm to leverage resources and services near the end-user, thereby providing prompt response to delay-sensitive real-time applications. Nevertheless, the tremendous amount of data generated by various IoT devices and sent over the network is always an open security challenge. The introduction of Federated Learning (FL) addresses the security and data privacy shortcomings of traditional centralized machine learning. Despite FL’s use for data privacy, it must overcome a number of significant challenges, such as privacy concerns, communication overhead, stragglers, and heterogeneity. To solve these challenges, this paper proposes a novel technique for enhancing security in IoT-enabled edge cloud computing networks, utilizing blockchain-driven FL and Gaussian Bayesian transfer convolutional neural network architectures for data analysis. Blockchain-driven FL ensures the security and privacy of consumer IoT applications. In comparison to state-of-the-art works, the experimental results achieved throughput of up to 89%, latency of 71%, training accuracy of 91%, validation accuracy of 96%, and network security of 92%.
Nebojša Bačanin, Marko Vićentijevic, Luka Jovanović, Angelina Njeguš · 6 authors
The decentralized architecture of blockchain tech-nology introduces various challenges, particularly in Proof of Stake (PoS) networks, where malicious nodes can undermine the consensus process and threaten the integrity of the network. Nodes with significant stakes may disproportionately influence validation, weakening the network's decentralization. Despite ongoing advancements in blockchain security, there remains a notable gap in research on the integration of artificial intelli-gence (AI) and optimization techniques for enhancing blockchain security. This paper proposes an approach, utilizing a CatBoost classifier optimized with a modified Particle Swarm Optimization (PSO) algorithm, designed specifically for this study. A compar-ative analysis, performed on a real-world dataset, demonstrates the effectiveness of the proposed method, with the most accurate models achieving an accuracy of 87.13%. The findings highlight the potential of AI -driven optimization in improving blockchain security. Future research will focus on the application of AI and optimization techniques to further refine blockchain protocols, enhance detection and prevention of security threats, and improve overall network performance.
Ahmed Ayoub Bellachia, Mouhamed Amine Bouchiha, Yacine Ghamri-Doudane, Mourad Rabah
Blockchain-based Federated Learning (BFL) is an emerging decentralized machine learning paradigm that enables model training without relying on a central server. Although some BFL frameworks are considered privacy-preserving, they are still vulnerable to various attacks, including inference and model poisoning. Additionally, most of these solutions employ strong trust assumptions among all participating entities or introduce incentive mechanisms to encourage collaboration, making them susceptible to multiple security flaws. This work presents VerifBFL, a trustless, privacy-preserving, and verifiable federated learning framework that integrates blockchain technology and cryptographic protocols. By employing zero-knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARKs) and in-crementally verifiable computation (IVC), VerifBFL ensures the verifiability of both local training and aggregation processes. The proofs of training accuracy and aggregation are verified on-chain, guaranteeing the integrity and auditability of each participant's contributions. To protect training data from inference attacks, VerifBFL leverages differential privacy. Finally, to demonstrate the efficiency of the proposed protocols, we built a proof of concept using emerging tools. The results show that generating proofs for local training and aggregation in VerifBFL takes less than 81s and 2s, respectively, while verifying them on-chain takes less than 0.6s.
N. Sethu Subramanian, Prabhakar Krishnan, Kurunandan Jain, K. B. Aneesh Kumar · 6 authors
5G (Fifth Generation) technology represents a significant advancement in telecommunications, facilitating industry innovation and advancement for applications across various sectors. It enhances high-speed, low-latency communication, making it ideal for task offloading in resource-constrained mobile devices. By leveraging task offloading, 5G networks maximize the efficiency of both computational and network resources, ensuring faster, more reliable data delivery and enabling high-performance requirements of modern applications. However, dynamic and secure task offloading remains a challenge due to fluctuating network conditions and trust concerns. This paper proposes SAGE (Secured Adaptive Generalized Edge), a reinforcement learning-based task offloading framework that leverages contextual bandits for adaptive decision-making in Multi-Access Edge Computing (MEC) environments. By integrating blockchain smart contracts for security and SDN-based orchestration for dynamic resource management, SAGE ensures robust, low-latency offloading. Experimental evaluations demonstrate that SAGE reduces task offloading delays by 36% and task durations by 30% compared to baseline methods under varying load and energy constraints.
The integration of Internet of Things (IoT) devices into smart environments has become increasingly prevalent, resulting in the collection of valuable user and service data. However, effectively utilizing this data often requires its aggregation on a central server to train algorithms capable of identifying and preventing malicious attacks, such as reconnaissance, DoS (Denial of service), DDoS (Distributed denial of service) within IoT networks. This transmission of raw data not only incurs substantial bandwidth costs but also raises significant privacy concerns. In this paper, we propose a federated learning framework for intrusion detection on IoT networks that incorporates a distributed storage system based on the Ethereum blockchain, enhancing the security of the federated learning process. This design offers several key benefits, including scalability, high availability, redundancy, and the capacity to process large datasets. Despite these advantages, relying solely on federated learning may not yield accurate results, particularly when dealing with highly imbalanced datasets. To address this challenge, we have integrated a diffusion model for data augmentation at each local node, which strengthens model robustness. Furthermore, to protect data privacy at each local node, we utilize transmitting and averaging model parameters instead of raw data. The proposed framework is trained and evaluated in two datasets. The MNIST (Modified National Institute of Standards and Technology) dataset and BoT-IoT dataset. Our results indicate significant improvements in detecting zero-day attacks, achieving an average F1-score of 98.3% on the short version of the BoT-IoT dataset as well.
Manjula K. Pawar, Prakashgoud Patil, D. G. Narayan, Vasundhara Pandey · 6 authors
Blockchain’s decentralized, transparent, and immutable nature has revolutionized digital transactions by removing the need for central authorities. Ethereum stands out among blockchain platforms for facilitating secure peer-to-peer transactions via smart contracts. Despite its transformative potential, blockchain faces challenges, particularly with the PoW consensus algorithm, which demands high energy consumption and raises centralization concerns. This affects the scalability of Blockchain by reducing the throughput. This paper explores machine learning (ML) integration to address these challenges, specifically focusing on optimizing miner selection in the Ethereum blockchain based on predicted transaction times. The study compares the performance of various machine learning models, including ElasticNet, Lasso Regression, Multilayer Perceptron (MLP) Regression in optimizing miner selection for reduced transaction times on the Ethereum blockchain. This study advances the ongoing research on integrating machine learning with blockchain to address the shortcomings of traditional Proof of Work (PoW) systems. It emphasizes the potential of machine learning to propel future innovations in blockchain technology.
Neural network inference in cloud service offers tangible benefits to users, from individuals and small institutions to large companies. However, two crucial concerns must be addressed. The first arises in satisfying the privacy of the model, the input data, and the inference results throughout the inference process. The second pertains to verifying that the inferences are derived from the designated neural network model. Although Secure Multi-Party Computation (MPC) and Zero-Knowledge Proof (ZKP) are typically adopted to mitigate such issues, the major challenge lies in achieving privacy preservation and verifiability simultaneously. In this study, we address both issues by proposing VSecNN, a verifiable and privacy-preserving neural network inference scheme. Specifically, we integrate MPC with the Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARK) protocol to achieve zero-knowledge proof generation for multiple parties. Subsequently, we perform adaptive optimizations on the multi-party proof generation approach to align with the neural network, thereby achieving both privacy-preserving capabilities and verifiability. Experimental results demonstrate an improvement in the efficiency. For example, the computation time for completing our multi-party proof generation could be as low as 1.7 times that of the single-party proof generation, while the verification requires only 169ms on the MNIST dataset.
Liping Tao, Yang Lu, Yuqi Fan, Lei Shi · 5 authors
Blockchain technology has garnered significant attention from academia and industry, with scalability remaining a key challenge. Sharding is a promising solution, dividing the blockchain into smaller partitions called shards, each processing a portion of the transactions to increase throughput. This approach is critical for enabling efficient Proof of Stake (PoS) consensus mechanisms, as demonstrated by the transition of Dogecoin to PoS, where sharding reduces the computational burden on validators and enhances scalability. However, sharding introduces high storage redundancy, as nodes in each shard must collectively maintain a copy of the entire blockchain, imposing substantial storage pressure. To address this, segments are introduced to divide the main chain into smaller parts distributed across nodes. Existing methods, however, randomly assign segments to nodes, resulting in high costs for node setup and segment queries. This paper investigates the optimal allocation of segments within shards to minimize these costs, proposing a Segment Allocation algorithm based on Cost Clustering (SACC). Theoretical analysis and simulations demonstrate that SACC achieves lower setup, query, and total costs while maintaining security and scalability, offering a more efficient solution for sharding-based PoS blockchains like Dogecoin.