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Connected health is a multidisciplinary approach focused on health management, prioritizing pa-tient needs in the creation of tools, services, and treatments. This paradigm ensures proactive and efficient care by facilitating the timely…
The application of Digital Twin (DT) technology and Federated Learning (FL) has great potential to change the field of biomedical image analysis, particularly for Computed Tomography (CT) scans. This paper presents Federated Transfer…
Metaverse-enabled digital healthcare systems are expected to exploit an unprecedented amount of personal health data, while ensuring that sensitive or private information of individuals are not disclosed. Machine learning and artificial…
The secondary use of healthcare data is vital for research and clinical innovation, but it raises concerns about patient privacy. This study investigates how to balance privacy preservation and data utility in healthcare data sharing,…
PeopleTraffic is a proposed initiative to develop a real-time, open-data population density mapping tool open to public institutions, private companies and the civil society, providing a common framework for infection spreading prevention.…
Personalized chronic care requires the integration of multimodal health data to enable precise, adaptive, and preventive decision-making. Yet most current digital twin (DT) applications remain organ-specific or tied to isolated data types,…
Mobile healthcare system integrating wearable sensing and wireless communication technologies continuously monitors the users' health status. However, the mHealth system raises a severe privacy concern as the data it collects are private…
Distributed Hash Tables (DHT) are a common architecture for decentralized applications and, therefore, would be suited for privacy-aware applications. However, currently existing DHTs allow every peer to access any index. To build…
Medical data is often highly sensitive in terms of data privacy and security concerns. Federated learning, one type of machine learning techniques, has been started to use for the improvement of the privacy and security of medical data. In…
Everyday, large amounts of sensitive data is distributed across mobile phones, wearable devices, and other sensors. Traditionally, these enormous datasets have been processed on a single system, with complex models being trained to make…
Robots have potential to revolutionize the way we interact with the world around us. One of their largest potentials is in the domain of mobile health where they can be used to facilitate clinical interventions. However, to accomplish this,…
Personal health libraries (PHLs) provide a single point of secure access to patients digital health data and enable the integration of knowledge stored in their digital health profiles with other sources of global knowledge. PHLs can help…
At this moment, databanks worldwide contain brain images of previously unimaginable numbers. Combined with developments in data science, these massive data provide the potential to better understand the genetic underpinnings of brain…
Machine learning models are becoming commonplace in the domain of medical imaging, and with these methods comes an ever-increasing need for more data. However, to preserve patient anonymity it is frequently impractical or prohibited to…
Federated learning (FL) is a decentralized method enabling hospitals to collaboratively learn a model without sharing private patient data for training. In FL, participant hospitals periodically exchange training results rather than…
Privacy-preserving and secure data sharing are critical for medical image analysis while maintaining accuracy and minimizing computational overhead are also crucial. Applying existing deep neural networks (DNNs) to encrypted medical data is…
Some machine learning applications involve training data that is sensitive, such as the medical histories of patients in a clinical trial. A model may inadvertently and implicitly store some of its training data; careful analysis of the…
Data privacy and ownership are significant in social data science, raising legal and ethical concerns. Sharing and analyzing data is difficult when different parties own different parts of it. An approach to this challenge is to apply…
The massive deployment of Machine Learning (ML) models raises serious concerns about data protection. Privacy-enhancing technologies (PETs) offer a promising first step, but hard challenges persist in achieving confidentiality and…
Modern healthcare systems now rely on advanced computing methods and technologies, such as Internet of Things (IoT) devices and clouds, to collect and analyze personal health data at an unprecedented scale and depth. Patients, doctors,…