Empowering Precision Medicine: Unlocking Revolutionary Insights through Blockchain-Enabled Federated Learning and Electronic Medical Records

Precision medicine has emerged as a transformative approach to healthcare, aiming to deliver personalized treatments and therapies tailored to individual patients. However, the realization of precision medicine relies heavily on the availability of comprehensive and diverse medical data. In this context, blockchain-enabled federated learning, coupled with electronic medical records (EMRs), presents a groundbreaking solution to unlock revolutionary insights in precision medicine. This abstract explores the potential of blockchain technology to empower precision medicine by enabling secure and decentralized data sharing and analysis. By leveraging blockchain’s immutability, transparency, and cryptographic protocols, federated learning can be conducted on distributed EMR datasets without compromising patient privacy. The integration of blockchain technology ensures data integrity, traceability, and consent management, thereby addressing critical concerns associated with data privacy and security. Through the federated learning paradigm, healthcare institutions and research organizations can collaboratively train machine learning models on locally stored EMR data, without the need for data centralization. The blockchain acts as a decentralized ledger, securely recording the training process and aggregating model updates while preserving data privacy at its source. This approach allows the discovery of patterns, correlations, and novel insights across a wide range of medical conditions and patient populations. By unlocking revolutionary insights through blockchain-enabled federated learning and EMRs, precision medicine can revolutionize healthcare delivery. This paradigm shift has the potential to improve diagnosis accuracy, optimize treatment plans, identify subpopulations for clinical trials, and expedite the development of novel therapies. Furthermore, the transparent and auditable nature of blockchain technology enhances trust among stakeholders, enabling greater collaboration, data sharing, and collective intelligence in the pursuit of advancing precision medicine. In conclusion, this abstract highlights the transformative potential of blockchain-enabled federated learning in empowering precision medicine. By unlocking revolutionary insights from diverse and distributed EMR datasets, this approach paves the way for a future where healthcare is personalized, efficient, and tailored to the unique needs of each patient

Machine Learning based Predictive Modelling of Cybersecurity Threats Utilising Behavioural Data

With the rapid advancement of technology in Malaysia, the number of cybercrimes is also increasing. To stop the increase in cybercrimes, everyone, including normal citizens, needs to know how secure they are while using digital appliances. A system is developed to predict the risk of users based on their behaviour when they are online using real-life behavioural data obtained from a private university’s 207 undergraduates. Five supervised machine learning methods are being tested which are: Regression Logistics, K-Nearest Neighbour (KNN), Decision Tree (DT), Support Vector Machine (SVM), and Naïve Bayesian Classifier with the aid of a tool, RapidMiner. The algorithms are used to construct, test, and validate three categories of cybercrime threat (Malware, Social Engineering, and Password Attack) predictive models. It was found that KNN model produces the highest accuracy and lowest classification error for all three categories of cybercrime threat. This system is believed to be crucial in alerting users with details of whether the consumer behaviour risk is high or low and what further actions can be taken to increase awareness. This system aims to prevent the rise in cybercrimes by providing a prediction of their risk levels in cybersecurity to encourage them to be more proactive in cybersecurity

An industrial IoT-based blockchain-enabled secure searchable encryption approach for healthcare systems using neural network

The IoT refers to the interconnection of things to the physical network that is embedded with software, sensors, and other devices to exchange information from one device to the other. The interconnection of devices means there is the possibility of challenges such as security, trustworthiness, reliability, confidentiality, and so on. To address these issues, we have proposed a novel group theory (GT)-based binary spring search (BSS) algorithm which consists of a hybrid deep neural network approach. The proposed approach effectively detects the intrusion within the IoT network. Initially, the privacy-preserving technology was implemented using a blockchain-based methodology. Security of patient health records (PHR) is the most critical aspect of cryptography over the Internet due to its value and importance, preferably in the Internet of Medical Things (IoMT). Search keywords access mechanism is one of the typical approaches used to access PHR from a database, but it is susceptible to various security vulnerabilities. Although blockchain-enabled healthcare systems provide security, it may lead to some loopholes in the existing state of the art. In literature, blockchainenabled frameworks have been presented to resolve those issues. However, these methods have primarily focused on data storage and blockchain is used as a database. In this paper, blockchain as a distributed database is proposed with a homomorphic encryption technique to ensure a secure search and keywords-based access to the database. Additionally, the proposed approach provides a secure key revocation mechanism and updates various policies accordingly. As a result, a secure patient healthcare data access scheme is devised, which integrates blockchain and trust chain to fulfill the efficiency and security issues in the current schemes for sharing both types of digital healthcare data. Hence, our proposed approach provides more security, efficiency, and transparency with cost-effectiveness. We performed our simulations based on the blockchain-based tool Hyperledger Fabric and OrigionLab for analysis and evaluation. We compared our proposed results with the benchmark models, respectively. Our comparative analysis justifies that our proposed framework provides better security and searchable mechanism for the healthcare system

A Novel Secure Blockchain Framework for Accessing Electronic Health Records Using Multiple Certificate Authority

Blockchain is a promising technology in the context of digital healthcare systems, but there are issues related to the control of accessing the electronic health records. In this paper, we propose a novel framework based on blockchain and multiple certificate authority that implement smart contracts and access health records securely. Our proposed solution provides the facilities of flexible policies to update a record or invoke the policy such that a patient has complete authority. A novel approach towards multiple certificate’s authority (CA) is introduced in the design through our proposed framework. Our proposed policies and methods overcome the shortcoming and security breaches faced by single certificate authority. Our proposed scheme provides a flexible access control mechanism for securing electronic health records as compared to the existing benchmark models. Moreover, our proposed method provides a re-enrolment facility in the case of a user lost enrolment

Security, privacy, and reliability in digital healthcare systems using blockchain

According to the security breach level index, millions of records are stolen worldwide on every single day. Personal health records are the most targeted records on the internet, and they are considered sensitive, and valuable. Security and privacy are the most important parameters of cryptography and encryption. They reduce the availability of data on patients and healthcare to the appropriate personnel and ultimately lead to a barrier in the transfer of healthcare into a digital health system. Using a permission blockchain to share healthcare data can reduce security and privacy issues. According to the literature, most healthcare systems rely on a centralized system, which is more prone to security vulnerabilities. The existing blockchain-based healthcare schemes provide only a data-sharing framework, but they lack security and privacy. To cope with these kinds of security issues, we have designed a novel security algorithm that provides security as well as privacy with much better efficiency and a lower cost. Hence, in this research, we have proposed a patient healthcare framework that provides greater security, reliability, and authentication compared to existing blockchain-based access control

A lightweight hybrid deep learning privacy preserving model for FC-based industrial internet of medical things

The Industrial Internet of Things (IIoT) is gaining importance as most technologies and applications are integrated with the IIoT. Moreover, it consists of several tiny sensors to sense the environment and gather the information. These devices continuously monitor, collect, exchange, analyze, and transfer the captured data to nearby devices or servers using an open channel, i.e., internet. However, such centralized system based on IIoT provides more vulnerabilities to security and privacy in IIoT networks. In order to resolve these issues, we present a blockchain-based deep-learning framework that provides two levels of security and privacy. First a blockchain scheme is designed where each participating entities are registered, verified, and thereafter validated using smart contract based enhanced Proof of Work, to achieve the target of security and privacy. Second, a deep-learning scheme with a Variational AutoEncoder (VAE) technique for privacy and Bidirectional Long Short-Term Memory (BiLSTM) for intrusion detection is designed. The experimental results are based on the IoT-Botnet and ToN-IoT datasets that are publicly available. The proposed simulations results are compared with the benchmark models and it is validated that the proposed framework outperforms the existing system

A novel hybrid trustworthy decentralized authentication and data preservation model for digital healthcare IoT based CPS

Digital healthcare is a composite infrastructure of networking entities that includes the Internet of Medical Things (IoMT)-based Cyber-Physical Systems (CPS), base stations, services provider, and other concerned components. In the recent decade, it has been noted that the demand for this emerging technology is gradually increased with cost-effective results. Although this technology offers extraordinary results, but at the same time, it also offers multifarious security perils that need to be handled effectively to preserve the trust among all engaged stakeholders. For this, the literature proposes several authentications and data preservation schemes, but somehow they fail to tackle this issue with effectual results. Keeping in view, these constraints, in this paper, we proposed a lightweight authentication and data preservation scheme for IoT based-CPS utilizing deep learning (DL) to facilitate decentralized authentication among legal devices. With decentralized authentication, we have depreciated the validation latency among pairing devices followed by improved communication statistics. Moreover, the experimental results were compared with the benchmark models to acknowledge the significance of our model. During the evaluation phase, the proposed model reveals incredible advancement in terms of comparative parameters in comparison with benchmark models

Deep learning based homomorphic secure search-able encryption for keyword search in blockchain healthcare system:A novel approach to cryptography

Due to the value and importance of patient health records (PHR), security is the most critical feature of encryption over the Internet. Users that perform keyword searches to gain access to the PHR stored in the database are more susceptible to security risks. Although a blockchain-based healthcare system can guarantee security, present schemes have several flaws. Existing techniques have concentrated exclusively on data storage and have utilized blockchain as a storage database. In this research, we developed a unique deep-learning-based secure search-able blockchain as a distributed database using homomorphic encryption to enable users to securely access data via search. Our suggested study will increasingly include secure key revocation and update policies. An IoT dataset was used in this research to evaluate our suggested access control strategies and compare them to benchmark models. The proposed algorithms are implemented using smart contracts in the hyperledger tool. The suggested strategy is evaluated in comparison to existing ones. Our suggested approach significantly improves security, anonymity, and monitoring of user behavior, resulting in a more efficient blockchain-based IoT system as compared to benchmark models