相关论文: MicroShare: Privacy-Preserved Medical Resource Sha…
This research proposes a decentralized and cryptographically secure framework to address the most acute issues of privacy, data security, and protection in the ecosystem of medical insurance claim processing. The scope of this study focuses…
Protected Health Information (PHI) sharing significantly enhances patient care quality and coordination, contributing to more accurate diagnoses, efficient treatment plans, and a comprehensive understanding of patient history. Compliance…
Clinicians routinely navigate fragmented electronic health record (EHR) interfaces to assemble a coherent picture of a patient's problems, medications, recent encounters, and longitudinal trends. This manuscript describes EHRSummarizer, a…
The digital transformation of the medical sector requires solutions that are convenient and efficient for all stakeholders while protecting patients' sensitive data. One example that has already attracted design-oriented research are…
Advancements in machine learning (ML) have significantly revolutionized medical image analysis, prompting hospitals to rely on external ML services. However, the exchange of sensitive patient data, such as chest X-rays, poses inherent…
Today, vast amounts of location data are collected by various service providers. These location data owners have a good idea of where their users are most of the time. Other businesses also want to use this information for location…
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…
E-health services handle a massive amount of sensitive data, requiring reliability and privacy. The advent of new technologies drives e-health services into their continuous provision outside traditional care institutions. This creates…
Sharing electronic health records (EHRs) on a large scale may lead to privacy intrusions. Recent research has shown that risks may be mitigated by simulating EHRs through generative adversarial network (GAN) frameworks. Yet the methods…
Healthcare blockchains provide an innovative way to store healthcare information, execute healthcare transactions, and build trust for healthcare data sharing and data integration in a decentralized open healthcare network environment.…
Deep learning continues to rapidly evolve and is now demonstrating remarkable potential for numerous medical prediction tasks. However, realizing deep learning models that generalize across healthcare organizations is challenging. This is…
With the advent of blockchain technology, multiple research avenues and platforms for dialogue have opened up. However technology transfer to the pubic has not been implemented, such that regular public can access and make use of secure and…
Deep-learning-as-a-service is a novel and promising computing paradigm aiming at providing machine/deep learning solutions and mechanisms through Cloud-based computing infrastructures. Thanks to its ability to remotely execute and train…
Objective: To (1) demonstrate the implementation of a data science platform built on open-source technology within a large, academic healthcare system and (2) describe two computational healthcare applications built on such a platform.…
Location-based services offer immense utility, but also pose significant privacy risks. In response, we propose LocPIR, a novel framework using homomorphic encryption (HE), specifically the TFHE scheme, to preserve user location privacy…
As cloud providers push multi-tenancy to new levels to meet growing scalability demands, ensuring that externally developed untrusted microservices will preserve tenant isolation has become a high priority. Developers, in turn, lack a means…
Personalized health analytics increasingly rely on population benchmarks to provide contextual insights such as ''How do I compare to others like me?'' However, cohort-based aggregation of health data introduces nontrivial privacy risks,…
Energy has been increasingly generated or collected by different entities on the power grid (e.g., universities, hospitals and householdes) via solar panels, wind turbines or local generators in the past decade. With local energy, such…
Electronic health records (EHRs) provide comprehensive patient data which could be better used to enhance informed decision-making, resource allocation, and coordinated care, thereby optimising healthcare delivery. However, in mental…
Electronic medical data sharing between stakeholders, such as patients, doctors, and researchers, can promote more effective medical treatment collaboratively. These sensitive and private data should only be accessed by authorized users.…