Blockchain Enabled Anonymous Privacy-Preserving Authentication Scheme for Internet of Health Things

A comprehensive secure system enabling healthcare 5.0 using federated learning, intrusion detection and blockchain

Recently, the use of the Internet of Medical Things (IoMT) has gained popularity across various sections of the health sector. The historical security risks of IoMT devices themselves and the data flowing from them are major concerns. Deploying many devices, sensors, services, and networks that connect the IoMT systems is gaining popularity. This study focuses on identifying the use of blockchain in innovative healthcare units empowered by federated learning. A collective use of blockchain with intrusion detection management (IDM) is beneficial to detect and prevent malicious activity across the storage nodes. Data accumulated at a centralized storage node is analyzed with the help of machine learning algorithms to diagnose disease and allow appropriate medication to be prescribed by a medical healthcare professional. The model proposed in this study focuses on the effective use of such models for healthcare monitoring. The amalgamation of federated learning and the proposed model makes it possible to reach 93.89 percent accuracy for disease analysis and addiction. Further, intrusion detection ensures a success rate of 97.13 percent in this study.

Blockchain technology and federated machine learning for collaborative initiatives in orthodontics and craniofacial health

There is a paucity of largescale collaborative initiatives in orthodontics and craniofacial health. Such nationally representative projects would yield findings that are generalizable. The lack of large-scale collaborative initiatives in the field of orthodontics creates a deficiency in study outcomes that can be applied to the population at large. The objective of this study is to provide a narrative review of potential applications of blockchain technology and federated machine learning to improve collaborative care. We conducted a narrative review of articles published from 2018 to 2023 to provide a high level overview of blockchain technology, federated machine learning, remote monitoring, and genomics and how they can be leveraged together to establish a patient centered model of care. To strengthen the empirical framework for clinical decision making in healthcare, we suggest use of blockchain technology and integrating it with federated machine learning. There are several challenges to adoption of these technologies in the current healthcare ecosystem. Nevertheless, this may be an ideal time to explore how best we can integrate these technologies to deliver high quality personalized care. This article provides an overview of blockchain technology and federated machine learning and how they can be leveraged to initiate collaborative projects that will have the patient at the center of care.

Human-centred artificial intelligence for mobile health sensing: challenges and opportunities

Advances in wearable sensing and mobile computing have enabled the collection of health and well-being data outside of traditional laboratory and hospital settings, paving the way for a new era of mobile health. Meanwhile, artificial intelligence (AI) has made significant strides in various domains, demonstrating its potential to revolutionize healthcare. Devices can now diagnose diseases, predict heart irregularities and unlock the full potential of human cognition. However, the application of machine learning (ML) to mobile health sensing poses unique challenges due to noisy sensor measurements, high-dimensional data, sparse and irregular time series, heterogeneity in data, privacy concerns and resource constraints. Despite the recognition of the value of mobile sensing, leveraging these datasets has lagged behind other areas of ML. Furthermore, obtaining quality annotations and ground truth for such data is often expensive or impractical. While recent large-scale longitudinal studies have shown promise in leveraging wearable sensor data for health monitoring and prediction, they also introduce new challenges for data modelling. This paper explores the challenges and opportunities of human-centred AI for mobile health, focusing on key sensing modalities such as audio, location and activity tracking. We discuss the limitations of current approaches and propose potential solutions.

Limitations and Future Aspects of Communication Costs in Federated Learning: A Survey

This paper explores the potential for communication-efficient federated learning (FL) in modern distributed systems. FL is an emerging distributed machine learning technique that allows for the distributed training of a single machine learning model across multiple geographically distributed clients. This paper surveys the various approaches to communication-efficient FL, including model updates, compression techniques, resource management for the edge and cloud, and client selection. We also review the various optimization techniques associated with communication-efficient FL, such as compression schemes and structured updates. Finally, we highlight the current research challenges and discuss the potential future directions for communication-efficient FL.

A novel privacy-preserving biometric authentication scheme

Most existing secure biometric authentication schemes are server-centric, and users must fully trust the server to store, process, and manage their biometric data. As a result, users' biometric data could be leaked by outside attackers or the service provider itself. This paper first constructs the EDZKP protocol based on the inner product, which proves whether the secret value is the Euclidean distance of the secret vectors. Then, combined with the Cuproof protocol, we propose a novel user-centric biometric authentication scheme called BAZKP. In this scheme, all the biometric data remain encrypted during authentication phase, so the server will never see them directly. Meanwhile, the server can determine whether the Euclidean distance of two secret vectors is within a pre-defined threshold by calculation. Security analysis shows BAZKP satisfies completeness, soundness, and zero-knowledge. Based on BAZKP, we propose a privacy-preserving biometric authentication system, and its evaluation demonstrates that it provides reliable and secure authentication.