S. Murali, S. Kanaga Suba Raja, E. Kanimozhi, D. Akila · 6 authors
In recent years, concerns about the security of data exchanged between the Internet of Things (IoT) devices have increased with respect to information security. Data security and the prevention of critical information leaks are both facilitated by a security model. Using Blockchain technology, the issues with data security in IoT devices may be resolved. Decentralized ledger technology known as blockchain enables many useful applications, including distributed storage, consensus, encryption, and Machine-to-Machine (M2M) data transmission. This paper develops a cross-layer framework for protecting the sensitive data of IoT by applying Blockchain based Federated Learning model (BCFL). The data records are organized into blocks in the BCFL paradigm, and then encode and decode methods are used to send the data to the servers. Using the Federated learning method, the block size and coding redundancy factor are calculated in a block-by-block transmission manner. To protect the integrity of data, a hash function and a digital signature are computed for each block of data. By experimental results, it was shown that the proposed BCFL model attains higher detection accuracy and correctness of data with reduced encoding/decoding time and reconstruction time.
Some blockchain networks employ a distributed consensus algorithm featuring Byzantine fault tolerance. Notably, certain public chains, such as Cosmos and Tezos, which operate on a proof-of-stake mechanism, have adopted this algorithm. While it is commonly assumed that these blockchains maintain a nearly constant block creation time, empirical analysis reveals fluctuations in this interval; this phenomenon has received limited attention. In this paper, we propose a mathematical model to account for the processes of block propagation and validation within Byzantine fault-tolerant consensus blockchains, aiming to theoretically analyze the probability distribution of block time. First, we propose stochastic processes governing the broadcasting communications among validator nodes. Consequently, we theoretically demonstrate that the probability distribution of broadcast time among validator nodes adheres to the Gumbel distribution. This finding indicates that the distribution of block time typically arises from convolving multiple Gumbel distributions. Additionally, we derive an approximate formula for the block time distribution suitable for data analysis purposes. By fitting this approximation to real-world block time data, we demonstrate the consistent estimation of block time distribution parameters,
V. Sunil Kumar, S. Renukadevi, Somashekhara Reddy, R Chandramma
By providing a secure and transparent platform for the exchange and storage of medical records, blockchain technology revolutionizes data management in the healthcare sector. Patient record confidentiality and integrity are ensured by this decentralized system, which forbids unauthorized access and tampering. Smart contracts speed up processes like insurance claims and simplify administrative work by enabling automated, trustless transactions. Additionally, blockchain facilitates seamless data- sharing by facilitating communication between various healthcare systems. Through a distributed ledger, medical professionals can access a patient’s whole medical history in real time, facilitating better diagnosis and treatment. Despite challenges like regulatory worries, blockchain’s potential for the healthcare industry holds promise for improved patient outcomes, security, and efficiency ( Kumar et al., 2021 ).
Jun Kong Phiang, Vivian Yong Siew Yee, Hafizuddin Bin Hilmi, Dedree Leonna Lai · 6 authors
The Industrial Internet of Things (IIoT) has revolutionized industrial processes, offering automation and data-driven decision-making. However, this interconnectedness brings new security challenges, especially in crucial infrastructure sectors. Traditional security measures are inadequate, leading to the exploration of innovative solutions. Blockchain technology has emerged as a promising solution due to its decentralized and immutable nature. This paper proposes a Hybrid Blockchain-Based Authentication Mechanism for IIoT, combining Delegated Proof of Stake (DPoS) and Elliptic Curve Cryptography (ECC). The hybrid architecture utilizes public and private blockchains to ensure scalability, efficiency, and security. Lightweight consensus algorithms, DPoS, are incorporated to optimize performance, while ECC provides efficient cryptographic techniques suitable for IIoT environments. An interoperable framework facilitates seamless integration with existing infrastructure, ensuring regulatory compliance and compatibility. Decentralized identity management further enhances security and privacy. Results and analysis demonstrate the effectiveness of the proposed solution, positioning hybrid blockchain architecture as the most suitable approach for enhancing security in IIoT environments.
Open access
Blockchain Technology Applications and Security
Physical Unclonable Functions (PUFs) and Hardware Security
This research presents an innovative framework that uses blockchain technology to improve tumor segmentation in medical imaging. The approach tackles issues related to data security, particularly when dealing with real private dataset, annotation accuracy, and collaboration. With the growing reliance of the medical industry on accurate tumor segmentation from medical images for cancer diagnosis and treatment, current methods are inadequate in maintaining data accuracy and promoting collaboration among experts across different countries. Our suggested approach utilizes blockchain technology to establish a decentralized, secure platform for the collaborative obtaining, annotation, and validation of medical images by data scientists, oncologists, and radiologists. Smart contracts streamline essential procedures such as verification of annotations, consensus among experts, and remuneration of contributors, guaranteeing the dependability and excellence of the data. Furthermore, the unchangeable record of transactions in the blockchain ensures a reliable basis for implementing artificial intelligence and machine learning algorithms. This improves the accuracy of segmenting data and allows for predictive modeling. This strategy not only improves the precision and effectiveness of tumor segmentation but also promotes a worldwide collaborative environment, which has the potential to revolutionize cancer diagnostics and treatment planning. Furthermore, it ensures the privacy and security of patient data.
Open access
Blockchain Technology Applications and Security
Artificial Intelligence in Healthcare and Education
Naveen Kumar Pandit, Swatisipra Das, Chandan Kumar Panda
The sharing of Electronic Health Records (EHR) has significant importance in healthcare. Cloud-based EHR sharing schemes have been used extensively to share patient records among various healthcare organizations. However, the centralization of the cloud may have an impact on the privacy and confidentiality of patient's medical data. The objective of this paper is to develop a Patient-centric EHR Management System using Ethereum Blockchain provides fine-grained access control with efficient EHR sharing to protect the security and privacy of patient EHR. We have employed an off-chain network Interplanetary File System (IPFS) and Ethereum blockchain, wherein encrypted data is stored off-chain and its metadata is saved on the Ethereum blockchain. To achieve a secure distributed trustworthy access policy, we proposed an Ethereum smart contract-based patient-centered access control. Where patients have full control of their EHR allowing them to grant and refuse access to the records.
Reshma Siyal, Jun Long, Muhammad Asim, Naveed Ahmad · 6 authors
Ensuring data confidentiality is a critical requirement for modern security systems globally. Despite the implementation of various access-control policies to enhance system security, significant threats persist due to insecure and inadequate access management. To address this, Multi-Party Authorization (MPA) systems employ multiple authorities for authorization and authentication, utilizing blockchain technology to store and access data securely, ensuring immutable and trusted audit trails. In this work, we propose a hybrid key-generation approach called the Identity and Attribute-Based Honey Encryption (IABHE) Algorithm combined with Deep Spiking Neural Network (DSNN) denoted by IABHE+DSNN for secure data sharing in a multi-party blockchain-based system. This approach incorporates various entities and multiple security functionalities to ensure data security. The data-sharing process involves several steps: initialization, authentication, initial registration, data protection, validation, and data sharing. Data protection is executed within the MapReduce framework, with data encryption performed using IABHE and key generation managed by DSNN. Experimental results demonstrate that the proposed IABHE+DSNN approach achieves a decryption time of 10.786 s, an encryption time of 15.765 s, and a key complexity of 0.887, outperforming existing methods.
Zhaohui Guo, Qiang Liu, Zhen Gao, Lei Liu · 8 authors
Asan emerging distributed ledger technology, blockchain provides multi-party trust between unreliable devices to share data and resources cooperatively, which facilitates the Internet of Vehicles (IoVs) applications. In the typical blockchain-enabled IoV (BIoV) scenarios, based on the data of common interest collected by various sensors, the IoV devices jointly maintain the world state in the form of address-balance pairs as the proof for transaction issuing and validation, where Ethereum-like blockchain is used as the finite-state machine driven by the transactions. However, with the extension of the IoV network, an enormous number of accounts leads to the explosive growth of the state data, which has been the main challenge for BIoV with resource-limited devices. This paper proposes a modular-based adaptive bit-width compression (ABC) scheme to reduce the state data storage on each device by representing the address as a remainder with a shorter bit-width. Besides, a new transaction validation method is designed with the support of the XOR filter, which guarantees that the core functions of the blockchain can still be performed normally with the proposed scheme applied. Theoretical analysis and simulation results show that the compression ratio for the address data could be more than 80%, which dramatically improves the scalability of BIoV system. In addition, the extra privacy-preserving property is introduced with the compression scheme because the account information is unrecoverable from the remainders.
Xiumei Deng, Jun Li, Long Shi, Wei, Kang · 8 authors
Digital twin (DT) has emerged as a promising solution to enhance manufacturing efficiency in industrial Internet of Things (IIoT) networks. To promote the efficiency and trustworthiness of DT for wireless IIoT networks, we propose a blockchain-enabled DT (B-DT) framework that employs deep neural network (DNN) partitioning technique and reputation-based consensus mechanism, wherein the DTs maintained at the gateway side execute DNN inference tasks using the data collected from their associated IIoT devices. First, we employ DNN partitioning technique to offload the top-layer DNN inference tasks to the access point (AP) side, which alleviates the computation burden at the gateway side and thereby improves the efficiency of DNN inference. Second, we propose a reputation-based consensus mechanism that integrates Proof of Work (PoW) and Proof of Stake (PoS). Specifically, the proposed consensus mechanism evaluates the off-chain reputation of each AP according to its computation resource contributions to the DNN inference tasks, and utilizes the off-chain reputation as a stake to adjust the block generation difficulty. Third, we formulate a stochastic optimization problem of communication resource (i.e., partition point) and computation resource allocation (i.e., computation frequency of APs for top-layer DNN inference and block generation) to minimize system latency under the time-varying channel state and long-term constraints of off-chain reputation, and solve the problem using Lyapunov optimization method. Experimental results show that the proposed dynamic DNN partitioning and resource allocation (DPRA) algorithm outperforms the baselines in terms of reducing the overall latency while guaranteeing the trustworthiness of the B-DT system.
Smart healthcare systems play a pivotal role in delivering accessible medical services, especially in remote environments where patients rely on wearable devices to collect medical data, subsequently transmitted to caregivers for diagnosis purpose. Despite the benefits of telehealth systems, their vulnerability to security breaches stemming from insecure communication channels highlights the critical need for robust mechanisms ensuring secure data transmission and access control. These mechanisms are essential to verify the legitimacy of both the patients and caregivers. To address these challenges, we propose ZKP-MAC, a lightweight Zero Knowledge proof-based authentication and access control scheme specifically tailored for Telehealth systems. Our proposed scheme integrates physical unclonable functions (PUFs), leveraging cryptographic keys derived from device hardware fingerprints to authenticate patient data. Additionally, we adopt a zero-knowledge proof protocol for caregivers' authentication and access control. By regenerating keys based on device hardware fingerprints, our approach ensures lightweight, secure, and authentic communication. Additionally, our model enables dynamic key management mechanism to regulate data access effectively and continuously in telehealth applications. Experimental results demonstrate the robustness of our approach against various security threats, offering features such as perfect backward secrecy and mutual access control. We conduct a comprehensive evaluation of the ZKP-MAC's security attributes, and computational overhead, showcasing reduced complexity compared to competing schemes while maintaining resilience against attempts to compromise security features by both individual and colluding actors. Furthermore, our scheme undergoes rigorous formal analysis using Automated Verification of Internet Security Protocols and Applications (A VISP A).
Blockchain-based steganography enables data hiding via encoding the covert data into a specific blockchain transaction field. However, previous works focus on the specific field-embedding methods while lacking a consideration on required field-generation embedding. In this paper, we propose GBSF, a generic framework for blockchain-based steganography. The sender generates the required fields, where the additional covert data is embedded to enhance the channel capacity. Based on GBSF, we design R-GAN that utilizes the generative adversarial network (GAN) with a reversible generator to generate the required fields and encode additional covert data into the input noise of the reversible generator. We then explore the performance flaw of R-GAN and introduce CCR-GAN as an improvement. CCR-GAN employs a counter-intuitive data preprocessing mechanism to reduce decoding errors in covert data. It incurs gradient explosion for model convergence and we design a custom activation function. We conduct experiments using the transaction amount of the Bitcoin mainnet as the required field. The results demonstrate that R-GAN and CCR-GAN allow to embed 11-bit (embedding rate of 17.2%) and 24-bit (embedding rate of 37.5%) covert data within a transaction amount, and enhance the channel capacity of state-of-the-art works by 4.30% to 91.67% and 9.38% to 200.00%, respectively.
Advanced Steganography and Watermarking Techniques
Diabetes poses a global health challenge, demanding continuous monitoring and expert care for effective management. Conventional monitoring methods lack real-time insights and secure data-sharing capabilities, necessitating innovative solutions that leverage emerging technologies. Existing centralized monitoring systems often entail risks such as data breaches and single points of failure, emphasizing the necessity for a secure, decentralized approach that integrates the Internet of Things (IoT), blockchain, and machine learning for efficient and secure diabetes management. This paper introduces a decentralized, blockchain-based framework for remote diabetes monitoring, IoT sensors, machine learning models, and decentralized applications (DApps). The proposed framework comprises five layers: the IoT Sensor Layer, which collects real-time health data from patients; the Blockchain Layer, leveraging smart contracts on the Ethereum blockchain for secure data sharing and transactions; the machine learning Layer, analyzing patient data to detect diabetes; and the DApps Layer, facilitating interactions between patients, doctors, and hospitals. For intelligent decision-making regarding diabetes based on data collected from different sensors, nine machine learning algorithms, including logistic regression, K-nearest neighbors (KNN), support vector machine (SVM), Decision Tree, Random Forest, AdaBoost, stochastic gradient boosting (SGD), and Naive Bayes, were trained and tested on the PIMA dataset. Based on the performance evaluation parameters such as accuracy, recall, F1-score, and the area under the curve (AUC), it was found that the AdaBoost model achieved the highest predictive accuracy of 92.64%, followed by the Decision Tree with an accuracy of 92.21% in diabetes classification.
Decentralized cryptocurrencies that operate on blockchains have received a lot of interest and have seen significant adoption in recent years. The initial blockchain application, Bitcoin, is a huge success and encourages further advancement in this area. However, Bitcoin experiences issues with limited throughput and long transaction times. It also affects other proof-of-work-related cryptocurrencies and raises fresh questions about the scaling of blockchain. The Scalability Issue in Blockchain is two-fold. Off-chain and On-chain On-chain like Block size increase, SegWit, Sharding, Proof-of-Work (PoW), Proof-of-Stake (PoS), or otherwise Off-Chain Lighting networks
In the future of healthcare, Blockchain (BC) technology holds immense potential for improving the security and privacy of data. By allowing the secure and immutable storage of medical files and healthcare-related transactions, BC ensured that sensitive medical data remains tamper-proof and open only to authorized parties. Patients have greater control over their data's development, revoking or granting access as required, but healthcare workers can streamline data sharing and ensure the integrity of important data. The decentralized nature of BC networks decreases the risk of centralized data breaches, eventually fostering trust and transparency in the healthcare ecosystems. Conversely, deep learning (DL) has great to revolutionize healthcare diagnostics in the future, offering quick and extremely accurate estimates of medical conditions. This technology has greatly enhanced patient solutions, decreased medical expenses, and improved the burden on medical staff by providing appreciated insights into an extensive range of conditions, from cancer to neurological disorders. With this stimulus, this study presents a novel BC with optimal DL-based secure data sharing and classification (BCODL-SDSC) technique in the future healthcare system. The goal of the BCODL-SDSC technique is to secure and thoroughly examine healthcare data using BC and DL techniques. Primarily, the BCODL-SDSC technique enables BC technology to store and maintain the patient’s data from the procedure of several transactions and enable access control to the various stakeholders. For the security of the medical images, the BCODL-SDSC technique applies the Fractional Order Lorenz system (FOLS) based encryption technique with tuna swarm optimization (TSO) algorithm based optimal key generation process. Finally, a multi-stage process performs the classification of the medical images: MobileNetv1 feature extractor, artificial rabbit’s optimization (ARO) based hyperparameter tuning, and stacked recurrent neural network (SRNN) based classification. The experimental outcome of the BCODL-SDSC technique was examined on a benchmark medical image database. An extensive comparative study reported that the BCODL-SDSC technique reaches an effective performance with other models with a maximum accuracy of 99.11%.
R. Sabitha, M Madhini, Priya Sethuraman, S. Vijayalakshmi · 5 authors
A key component of a blockchain is its distributed digital ledger of transactions, which is effectively a record of all transactions recorded in the network. A key component of a blockchain is its distributed digital ledger of transactions, which is effectively a record of all transactions recorded in the network. The process of altering images is been simplified. In a web-based program, a validation system for student certificates is created. The issue statement suggests that universities keep records of students who are unable to attend class in the form of a certificate. To skip class and get a doctor’s note is just too simple. A few pupils have been caught using forged certificates to skip class. Many people nowadays are dishonest and would buy or make fake certificates from websites that claim to provide them. Having to verify and validate certificates is a pain for the company and the institution. For safekeeping of certificates on the blockchain, we provide a method we term Blockchain Powered Student Certificate Validation (BPSCV). A standard Optical Character Recognition (OCR) model is used for cross-validation in order to assess how well the suggested task works. Digitization of the paper certificates is the initial step. When creating the certificate’s hash code, the suggested algorithm is utilized. Certificates are then recorded in the blockchain. Furthermore, the mobile app verifies these credentials. The use of blockchain technology allows us to validate digital certificates in a more efficient and safe manner.
Internet of Things (IoT) services necessitate the storage, transmission, and analysis of diverse data for inference, autonomy, and control. Blockchains, with their inherent properties of decentralization and security, offer efficient database solutions for these devices through consensus-based data sharing. However, it's essential to recognize that not every blockchain system is suitable for specific IoT applications, and some might be more beneficial when excluded with privacy concerns. For example, public blockchains are not suitable for storing sensitive data. This paper presents a detailed review of three distinct blockchains tailored for enhancing IoT applications. We initially delve into the foundational aspects of three blockchain systems, highlighting their strengths, limitations, and implementation needs. Additionally, we discuss the security issues in different blockchains. Subsequently, we explore the blockchain's application in three pivotal IoT areas: edge AI, communications, and healthcare. We underscore potential challenges and the future directions for integrating different blockchains in IoT. Ultimately, this paper aims to offer a comprehensive perspective on the synergies between blockchains and the IoT ecosystem, highlighting the opportunities and complexities involved.
The integration of blockchain technology into the federated learning (FL) process offers effective measures for safeguarding the security and privacy of model data. However, the inherent consensus mechanism of blockchain technology can introduce long latency, which may hinder the overall efficiency of FL. To address this challenge, we propose a blockchain-based FL training strategy that tackles the following issues: (1) Reducing the number of parameter aggregations in the blockchain network by increasing the number of local training epochs, which effectively minimize the frequency of blockchain authentication and packing operations. (2) Mitigating communication overhead between terminals and edge nodes in the blockchain network by leveraging the wait-free backpropagation technique, which reduces the communication overhead. Experimental results demonstrate that our proposed strategy yields improvements in both convergence efficiency and system scalability.
Cervical cancer is a serious health concern that entails high risks for individuals due to delayed detection and treatment worldwide. Formal screening for the condition is challenging in both developed and developing countries due to a number of factors, including medical costs, access to healthcare facilities, social norms, and delayed symptom manifestation. Bypassing conventional, time-consuming medical procedures, machine learning presents a promising path for the efficient and economical early diagnosis of a variety of diseases, including cervical cancer. However, the fact that existing machine classification techniques for identifying diseases rely heavily on the predictive accuracy of a single classifier poses a significant drawback. Single classification methods alone might not provide the best predictions because of bias, over-fitting, improper handling of noisy data, and outliers, among other issues. Moreover, machine learning algorithms deals with sensitive patient data therefore Security measures are necessary to prevent unauthorized access and safeguard individual and organisations’ privacy, guard against model tampering. This paper proposes a novel framework for cervical cancer automated prediction using ensemble model training and blockchain smart contracts. The research records a noteworthy improvement in prediction test accuracy of 99.7% and train accuracy of 93%, surpassing the accuracy of predictions made by individual categorization techniques.
Blockchain, a decentralized database safeguarded by cryptographic security, has gained prominence for its resistance to manipulation. Its application extends notably to ensuring the security of financial transactions, including the acquisition of digital currencies. This study endeavors to develop a system aimed at validating diplomas and academic transcripts, enhancing their authenticity and bolstering the security of document storage. Leveraging the Ethereum Blockchain and Smart Contracts, the methodology focuses on the utilization of specialized codes executed within the Ethereum network. The outcome of this research manifests as a system blueprint designed for the verification of diplomas and transcripts, integrated within a web-based Ethereum Network platform. By harnessing the Ethereum Blockchain's inherent security features and employing Smart Contracts, the proposed system endeavors to streamline the verification process, ensuring the integrity and reliability of academic credentials while fortifying document storage against potential breaches. Through this innovative approach, the study contributes to advancing the authentication and security standards within the realm of academic documentation management.
Healthcare institutions, including hospitals, clinics, and medical imaging centers, often encounter difficulties in sharing medical images across different systems and facilities. Further, most healthcare systems face concerns related to data security and patient privacy. This paper is based on the development of a blockchain-powered medical image storage and sharing platform. The platform’s architecture includes components such as smart contracts, encryption mechanisms, and decentralized storage systems, which collectively enable seamless and trustworthy medical image sharing. The use of smart contracts provides a reliable framework for access control and data sharing permissions. The encryption mechanisms safeguard sensitive patient information during transmission and storage, bolstering data security and privacy. Additionally, the utilization of decentralized storage systems ensures redundant and distributed data storage, mitigating the risk of data loss, manipulation, or security threats. The research underlying this project involves leveraging blockchain’s inherent properties of decentralization, immutability, and transparency to establish a secure and interoperable infrastructure for medical image exchange. By harnessing the potential of distributed ledger technology, the proposed platform addresses the existing challenges of fragmented systems, limited interoperability, and data silos in medical image sharing. Blockchain technology addresses critical challenges in medical image sharing, paving the way for enhanced collaboration, improved patient care, and increased efficiency in healthcare.
Blockchain Technology Applications and Security
Brain Tumor Detection and Classification
Artificial Intelligence in Healthcare and Education