相关论文: Securing Health Data on the Blockchain: A Differen…

Integrating blockchain technology into healthcare systems presents a transformative approach to documenting, storing, and accessing electronic health records (EHRs). This research introduces a novel blockchain-based EHR system designed to…

The Internet of Medical Things (IoMT) offers an infrastructure made of smart medical equipment and software applications for health services. Through the internet, the IoMT is capable of providing remote medical diagnosis and timely health…

Data integration among various stakeholders in the healthcare space remains a challenge, despite the impressive advances in Health AI in the past decade. There is a lot of ``messy'' non-standard but structured data that are continually…

Despite the great potential of Federated Learning (FL) in large-scale distributed learning, the current system is still subject to several privacy issues due to the fact that local models trained by clients are exposed to the central…

Protecting sensitive health data while enabling collaborative analysis is a central challenge in healthcare. Traditional machine learning (ML) requires institutions to pool anonymized patient records, centralizing analytical development and…

The development and adoption of Electronic Health Records (EHR) and health monitoring Internet of Things (IoT) Devices have enabled digitization of patient records and has also substantially transformed the healthcare delivery system in…

The advancement in the healthcare sector is entering into a new era in the form of Health 4.0. The integration of innovative technologies like Cyber-Physical Systems (CPS), Big Data, Cloud Computing, Machine Learning, and Blockchain with…

With the growing need to comply with privacy regulations and respond to user data deletion requests, integrating machine unlearning into IoT-based federated learning has become imperative. Traditional unlearning methods, however, often lack…

As edge computing gains prominence in Internet of Things (IoTs), smart cities, and autonomous systems, the demand for real-time machine intelligence with low latency and model reliability continues to grow. Federated Learning (FL) addresses…

Today's world is a globalized and connected one, where people are increasingly moving around and interacting with a greater number of services and devices of all kinds, including those that allow them to monitor their health. However, each…

Federated learning (FL) enhances privacy by keeping user data on local devices. However, emerging attacks have demonstrated that the updates shared by users during training can reveal significant information about their data. This has…

The growing utilization of Internet of Medical Things (IoMT) devices, including smartwatches and wearable medical devices, has facilitated real-time health monitoring and data analysis to enhance healthcare outcomes. These gadgets…

Federated Learning (FL) provides privacy preservation by allowing the model training at edge devices without the need of sending the data from edge to a centralized server. FL has distributed the implementation of ML. Another variant of FL…

Data privacy refers to ensuring that users keep control over access to information, whereas data accessibility refers to ensuring that information access is unconstrained. Conflicts between privacy and accessibility of data are natural to…

Privacy data protection in the medical field poses challenges to data sharing, limiting the ability to integrate data across hospitals for training high-precision auxiliary diagnostic models. Traditional centralized training methods are…

The transparent and decentralized characteristics associated with blockchain can be both appealing and problematic when applied to a healthcare use-case. As health data is highly sensitive, it is also highly regulated to ensure the privacy…

In a world where data is the new currency, wearable health devices offer unprecedented insights into daily life, continuously monitoring vital signs and metrics. However, this convenience raises privacy concerns, as these devices collect…

Federated learning (FL) is a distributed machine learning (ML) technique that enables collaborative training in which devices perform learning using a local dataset while preserving their privacy. This technique ensures privacy,…

Federated learning (FL) is a distributed machine learning approach that protects user data privacy by training models locally on clients and aggregating them on a parameter server. While effective at preserving privacy, FL systems face…

Classifying brain signals collected by wearable Internet of Things (IoT) sensors, especially brain-computer interfaces (BCIs), is one of the fastest-growing areas of research. However, research has mostly ignored the secure storage and…