相关论文: MicroShare: Privacy-Preserved Medical Resource Sha…
The recent advancements in computing systems and wireless communications have made healthcare systems more efficient than before. Modern healthcare devices can monitor and manage different health conditions of the patients automatically…
In this letter we propose Meta-key, a data-sharing mechanism that enables users share their encrypted data under a blockchain-based decentralized storage architecture. All the data-encryption keys are encrypted by the owner's public key and…
In the big data era, many organizations face the dilemma of data sharing. Regular data sharing is often necessary for human-centered discussion and communication, especially in medical scenarios. However, unprotected data sharing may also…
This study introduces the development of a state of the art, real time ECG monitoring and analysis system, incorporating cutting edge medical technology and innovative data security measures. Our system performs three distinct functions…
The advent of cost effective cloud computing over the past decade and ever-growing accumulation of high-fidelity clinical data in a modern hospital setting is leading to new opportunities for translational medicine. Machine learning is…
Electronic Health Records (EHRs) are integral for storing comprehensive patient medical records, combining structured data (e.g., medications) with detailed clinical notes (e.g., physician notes). These elements are essential for…
Traditional data collection, storage and processing of Electronic Health Records (EHR) utilize centralized techniques that pose several risks of single point of failure and lean the systems to a number of internal and external data breaches…
We present the design and implementation of the PeerShare, a system that can be used by applications to securely distribute sensitive data to social contacts of a user. PeerShare incorporates a generic framework that allows different…
Preserving the privacy of individuals by protecting their sensitive attributes is an important consideration during microdata release. However, it is equally important to preserve the quality or utility of the data for at least some…
Web browsers are increasingly used as middleware platforms offering a central access point for service provision. Using backend containerization, RESTful APIs, and distributed computing allows for complex systems to be realized that address…
This paper introduces SPOT, a Secure and Privacy-preserving prOximity based protocol for e-healthcare systems. It relies on a distributed proxy-based approach to preserve users' privacy and a semi-trusted computing server to ensure data…
A new technique for embedding data into an image coupled with compression has been proposed in this paper. A fast and efficient coding algorithms are needed for effective storage and transmission, due to the popularity of telemedicine and…
Sharing clinical research data is key for increasing the pace of medical discoveries that improve human health. However, concern about study participants' privacy, confidentiality, and safety is a major factor that deters researchers from…
The microservice software architecture leverages the idea of splitting large monolithic applications into multiple smaller services that interact using lightweight communication schemes. While the microservice architecture has proven its…
Major advances in telecommunications and the Internet of Things have given rise to numerous smart city scenarios in which smart services are provided. What was once a dream for the future has now become reality. However, the need to provide…
Privacy of the outsourced data is one of the major challenge.Insecurity of the network environment and untrustworthiness of the service providers are obstacles of making the database as a service.Collection and storage of personally…
This study examines integrating EHRs and NLP with large language models (LLMs) to improve healthcare data management and patient care. It focuses on using advanced models to create secure, HIPAA-compliant synthetic patient notes for…
Balancing the needs of data privacy and predictive utility is a central challenge for machine learning in healthcare. In particular, privacy concerns have led to a dearth of public datasets, complicated the construction of multi-hospital…
The increasing adoption of Cloud-based data processing and storage poses a number of privacy issues. Users wish to preserve full control over their sensitive data and cannot accept it to be fully accessible to an external storage provider.…
A cyber-physical system is considered to be a collection of strongly coupled communication systems and devices that poses numerous security trials in various industrial applications including healthcare. The security and privacy of patient…