A quantum trust and consultative transaction-based blockchain cybersecurity model for healthcare systems

Abstract

Many researchers have been interested in healthcare cybersecurity for a long time since it can improve the security of patient and health record data. As a result, a lot of research is done in the field of cybersecurity that focuses on the safe exchange of health data between patients and the medical setting. It still has issues with high computational complexity, increased time consumption, and cost complexity, all of which have an impact on the effectiveness and performance of the complete security system. Hence this work proposes a technique called Consultative Transaction Key Generation and Management (CTKGM) to enable secure data sharing in healthcare systems. It generates a unique key pair based on random values with multiplicative operations and time stamps. The patient data is then safely stored in discrete blocks of hash values using the blockchain methodology. The Quantum Trust Reconciliation Agreement Model (QTRAM), which calculates the trust score based on the feedback data, ensures reliable and secure data transfer. By allowing safe communication between patients and the healthcare system based on feedback analysis and trust value, the proposed framework makes a novel contribution to the field. Additionally, during communication, the Tuna Swarm Optimization (TSO) method is employed to validate nonce verification messages. Nonce message verification is a part of QTRAM that helps verify the users during transmission. The effectiveness of the suggested scheme has been demonstrated by comparing the obtained findings with other current state-of-the-art models after a variety of evaluation metrics have been analyzed to test the performance of this security model.

A scalable blockchain based framework for efficient IoT data management using lightweight consensus

“…It comprises a series of interconnected blocks where each block contains a hash of the previous block forming a continuous chain from the initial or” genesis” block to the most recent block. The genesis block holds a unique status as it does not refer to any previous block and is typically hardcoded into the software 3 . While there is only one direct path from any block to the genesis block, forks can occur from the genesis block onwards when two blocks are generated within a short timeframe.…”

“…Projections suggest this rise will continue potentially reaching 29.42 billion by 2030 2 . The prevalence of resource-constrained IoT devices that are affordable, cost-effective, and advanced information and communication technologies infrastructure has led to the widespread adoption of IoT networks for various applications, including healthcare, industry, smart homes, smart grids, security, surveillance, and more 3 , 4 . Traditionally, IoT networks have been established using centralized infrastructure and technology.…”

“…Because transactions in blockchain don't need third parties, they're super reliable. It has numerous inherent characteristics, including decentralization, immutability and transparency which offer significant benefits in terms of increased security, data protection from unauthorized access and complete process traceability 3 . Combining these emerging technologies can form secure and scalable IoT networks streamlining data exchange among devices, systems and stakeholders.…”

“…These challenges are overcome by consensus algorithms such as DPoS 16 . DPoS is a lightweight consensus algorithm to address some of the challenges associated with PoW and PoS 3 . This process maintains security by verifying all the legitimate transactions where DPoS leverages chosen numbers of elected delegates 16 .…”

A Deep Auto-Optimized Collaborative Learning (DACL) model for disease prognosis using AI-IoMT systems

“…The major goal of this work is to create an effective and automated AI-IoMT system for classifying the various chronic diseases using patient medical records. Some of the well-known and open-source datasets 46 , 47 , including the Kaggle heart disease (Table 3 ), Stroke dataset, diabetes disease dataset and Statlog dataset, were used to assess the efficacy and room for improvement of the proposed DACL model. To statistically assess the proposed framework, well-known and significant performance evaluation standards including Accuracy, Precision, Recall, F1-score, sensitivity, specificity, and fall-out rate have been utilized in this study.…”