相关论文: An Intelligent Quantum Cyber-Security Framework fo…
Concerns regarding privacy and data security in conventional healthcare prompted alternative technologies. In smart healthcare, blockchain technology addresses existing concerns with security, privacy, and electronic healthcare…
Cloud computing has made storing and accessing data easier but keeping it secure is a big challenge nowadays. Traditional methods of ensuring data may not be strong enough in the future when powerful quantum computers become available. To…
The application of machine learning (ML) algorithms are massively scaling-up due to rapid digitization and emergence of new tecnologies like Internet of Things (IoT). In today's digital era, we can find ML algorithms being applied in the…
In this paper, we introduce the Fully Homomorphic Integrity Model (HIM), a novel approach designed to enhance security, efficiency, and reliability in encrypted data processing, primarily within the health care industry. HIM addresses the…
In the era of digital health and artificial intelligence, the management of patient data privacy has become increasingly complex, with significant implications for global health equity and patient trust. This paper introduces a novel…
Technological advances in Artificial Intelligence (AI) and Machine Learning (ML) for the healthcare domain are rapidly arising, with a growing discussion regarding the ethical management of their development. In general, ML healthcare…
The increasing importance of data in the healthcare sector has led to a rise in cybercrime targeting patient information. Data breaches pose significant financial and reputational risks to many healthcare organizations including clinics and…
Focussing on two different use cases-Quality Control methods in industrial contexts and Neural Network algorithms for healthcare diagnostics-this research investigates the inclusion of Fully Homomorphic Encryption into real-world…
In response to increasingly sophisticated cyberattacks, a health-based approach is being used to define and assess their impact. Two significant cybersecurity workshops have fostered this perspective, aiming to standardize the understanding…
In recent years, many researchers have focused on developing a feasible solution for storing and exchanging medical images in the field of health care. Current practices are deployed on cloud-based centralized data centers, which increase…
Quantum Computing (QC) is transitioning from theoretical frameworks to an indispensable powerhouse of computational capability, resulting in extensive adoption across both industrial and academic domains. QC presents exceptional advantages,…
The advancement of Internet and Communication Technologies (ICTs) has led to the era of Industry 4.0. This shift is followed by healthcare industries creating the term Healthcare 4.0. In Healthcare 4.0, the use of IoT-enabled medical…
Internet of Things (IoT) devices are capable of allowing for far-reaching access to and evaluation of patient data to monitor health and diagnose from a distance. An electronic healthcare system that checks patient data, prepares medicines…
Dementia, a neurological disorder impacting millions globally, presents significant challenges in diagnosis and patient care. With the rise of privacy concerns and security threats in healthcare, federated learning (FL) has emerged as a…
Healthcare data contains sensitive information, and it is challenging to persuade healthcare data owners to share their information for research purposes without any privacy assurance. The proposed hybrid medical data privacy protection…
The rise of large-scale quantum computing poses a significant threat to traditional cryptographic security measures. Quantum attacks undermine current asymmetric cryptographic algorithms, rendering them ineffective. Even symmetric key…
Digital signatures represent a crucial cryptographic asset that must be protected against quantum adversaries. Quantum Digital Signatures (QDS) can offer solutions that are information-theoretically (IT) secure and thus immune to quantum…
The lack of data democratization and information leakage from trained models hinder the development and acceptance of robust deep learning-based healthcare solutions. This paper argues that irreversible data encoding can provide an…
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…
Blockchain is a distributed database which is cryptographically protected against malicious modifications. While promising for a wide range of applications, current blockchain platforms rely on digital signatures, which are vulnerable to…