Blockchain-enabled Secure Privacy-preserving System for Public Health-center Data

Health center data implicates a large scale of individual health records and is immensely concealment sensory. In the virtual era of large-size data, the increasingly different health informatization causes it important that health data needs to be stored precisely and securely. However, daily health data transactions carry the risk of privacy leaks that make sharing difficult. Moreover, the recently permitted blockchain applications suffer from deficient performance and lack of privacy. This study presents a privacy-preserving and secure sharing and storage system for public health centers based on the blockchain method to dispose of these issues. This system utilizes a hash-256-based access controller and transaction signature with the consensus policy and provides security to share and store health data in the blockchain. In this approach, blockchain guarantees scalability, privacy, integrity, and availability for data retention. Also, this paper measures the performance of transactions with supporting confidentiality-preserving and shows the average transaction time and acceptable latency when accessing health data

Fingerprint authentication-based traffic offence control and enforcement system on smart mobile devices for smart city

The evolution of communication and information technology in today's time should not despise the various practical aspects of daily life, regardless of economic, education, health, or other government services. Various functions of smart mobile devices meet the needs of users in many ways, where different mechanisms like templates, locks, fingerprints, and passwords are used to protect those functions. Due to the increased number of vehicles in smart cities, it becomes difficult for traffic officers with less manpower to complete many of their tasks related to registration, license, and issuance of summons in time, even reviewing the traffic violation's history. In addition, existing traffic systems are not real-time, data related to traffic management can be lost at any time, leading to the wastage of money and resources. To overcome these difficulties in smart cities, this paper proposes a fingerprint authentication-based traffic offence control-and-enforcement system on smart mobile devices. This scheme introduces a security framework to facilitate many tasks related to identification, registration, licensing, and issuance of summons to traffic violators by implementing fingerprint authentication. Functionality tests and user acceptance tests related to traffic offence problems have been conducted on the proposed system by analyzing biometric data of vehicle users' fingerprints

Blockchain-enabled cybersecurity provision for scalable heterogeneous network: A comprehensive survey

Blockchain-enabled cybersecurity system to ensure and strengthen decentralized digital transaction is gradually gaining popularity in the digital era for various areas like finance, transportation, healthcare, education, and supply chain management. Blockchain interactions in the heterogeneous network have fascinated more attention due to the authentication of their digital application exchanges. However, the exponential development of storage space capabilities across the blockchain-based heterogeneous network has become an important issue in preventing blockchain distribution and the extension of blockchain nodes. There is the biggest challenge of data integrity and scalability, including significant computing complexity and inapplicable latency on regional network diversity, operating system diversity, bandwidth diversity, node diversity, etc., for decision-making of data transactions across blockchain-based heterogeneous networks. Data security and privacy have also become the main concerns across the heterogeneous network to build smart IoT ecosystems. To address these issues, today’s researchers have explored the potential solutions of the capability of heterogeneous network devices to perform data transactions where the system stimulates their integration reliably and securely with blockchain. The key goal of this paper is to conduct a state-of-the-art and comprehensive survey on cybersecurity enhancement using blockchain in the heterogeneous network. This paper proposes a full-fledged taxonomy to identify the main obstacles, research gaps, future research directions, effective solutions, and most relevant blockchain-enabled cybersecurity systems. In addition, Blockchain based heterogeneous network framework with cybersecurity is proposed in this paper to meet the goal of maintaining optimal performance data transactions among organizations. Overall, this paper provides an in-depth description based on the critical analysis to overcome the existing work gaps for future research where it presents a potential cybersecurity design with key requirements of blockchain across a heterogeneous network

Healthcare-Chain: Blockchain-Enabled Decentralized Trustworthy System in Healthcare Management Industry 4.0 with Cyber Safeguard

The pervasiveness of healthcare data to create better healthcare facilities and opportunities is one of the most-imperative parts of human life that offers radical advancements in healthcare services practiced through the blockchain-based management, analysis, storage, and sharing of health-related big data. Researchers can accelerate the challenges of developing a secure, scalable, and accessible dynamic healthcare infrastructure by the extensive data exchange required through individual microservices of blockchain-based privacy-preserving health data management ledgers in Healthcare Industry 4.0. Conducting secure and privacy-preserving platforms through primitive cryptographic algorithms is risky and can be a serious concern as the need to authenticate and store sensitive health data automatically are increasingly high. To achieve interoperability, security, efficiency, scalability, availability, and accountability among healthcare providers in heterogeneous networks, this paper proposes a blockchain-enabled decentralized, trustworthy privacy-preserving platform in the healthcare industry. In the healthcare-chain system, blockchain provides an appreciated secure environment for the privacy-preserving health data management ledger through hash processing, which updates high data security, storage immutability, and authentication functionality with an integrated attribute signature in accessing prescribed health block data. This article describes a new secure data retention design, prescribed evidence collection, and evaluation mechanism with integrity–confidentiality–availability to enforce the data access control policies for transactions of healthcare microservices. This scheme revealed the optimal performance in terms of mining health data size, average response time, transaction latency, and throughput for secured block transactions in blockchain networks

Healthcare-Chain: Blockchain-Enabled Decentralized Trustworthy System in Healthcare Management Industry 4.0 with Cyber Safeguard

The pervasiveness of healthcare data to create better healthcare facilities and opportunities is one of the most-imperative parts of human life that offers radical advancements in healthcare services practiced through the blockchain-based management, analysis, storage, and sharing of health-related big data. Researchers can accelerate the challenges of developing a secure, scalable, and accessible dynamic healthcare infrastructure by the extensive data exchange required through individual microservices of blockchain-based privacy-preserving health data management ledgers in Healthcare Industry 4.0. Conducting secure and privacy-preserving platforms through primitive cryptographic algorithms is risky and can be a serious concern as the need to authenticate and store sensitive health data automatically are increasingly high. To achieve interoperability, security, efficiency, scalability, availability, and accountability among healthcare providers in heterogeneous networks, this paper proposes a blockchain-enabled decentralized, trustworthy privacy-preserving platform in the healthcare industry. In the healthcare-chain system, blockchain provides an appreciated secure environment for the privacy-preserving health data management ledger through hash processing, which updates high data security, storage immutability, and authentication functionality with an integrated attribute signature in accessing prescribed health block data. This article describes a new secure data retention design, prescribed evidence collection, and evaluation mechanism with integrity–confidentiality–availability to enforce the data access control policies for transactions of healthcare microservices. This scheme revealed the optimal performance in terms of mining health data size, average response time, transaction latency, and throughput for secured block transactions in blockchain networks

Effective combining of feature selection techniques for machine learning-enabled IoT intrusion detection

The rapid advancement of technologies has enabled businesses to carryout their activities seamlessly and revolutionised communications across the globe. There is a significant growth in the amount and complexity of Internet of Things devices that are deployed in a wider range of environments. These devices mostly communicate through Wi-Fi networks and particularly in smart environments. Besides the benefits, these devices also introduce security challenges. In this paper, we investigate and leverage effective feature selection techniques to improve intrusion detection using machine learning methods. The proposed approach is based on a centralised intrusion detection system, which uses the deep feature abstraction, feature selection and classification to train the model for detecting the malicious and anomalous actions in the traffic. The deep feature abstraction uses deep learning techniques of artificial neural network in the form of unsupervised autoencoder to construct more features for the traffic. Based on the availability of cumulative features, the system then employs a variety of wrapper-based feature selection techniques ranging from SVM and decision tree to Naive Bayes for selecting high-ranked features, which are then combined and fed into an artificial neural network classifier for distinguishing attack and normal behaviors. The experimental results reveal the effectiveness of the proposed method on Aegean Wi-Fi Intrusion Dataset, which achieves high detection accuracy of up to 99.95%, relatively competitive to the existing machine learning works for the same dataset