Related papers: Securing Health Data on the Blockchain: A Differen…
The integration of blockchain technology in Internet of Things (IoT) environments is a revolutionary step towards ensuring robust security and enhanced privacy. This paper delves into the unique challenges and solutions associated with…
In the light of the growing connectivity and sensitivity of industrial data, cyberattacks and data breaches are becoming more common in the Industrial Internet of Things (IIoT). To cope with such threats, this study presents an anomaly…
Electronic Health Records(EHR) are gaining a lot of popularity all over the world. The current EHR systems however have their fair share of problems related to privacy and security. We have proposed a mechanism which provides a solution to…
Federated learning enables training a global machine learning model from data distributed across multiple sites, without having to move the data. This is particularly relevant in healthcare applications, where data is rife with personal,…
With the increasing importance of data sharing for collaboration and innovation, it is becoming more important to ensure that data is managed and shared in a secure and trustworthy manner. Data governance is a common approach to managing…
Electronic government (e-government) uses information and communication technologies to deliver public services to individuals and organisations effectively, efficiently and transparently. E-government is one of the most complex systems…
The accelerated expansion of the Internet of Things (IoT) has raised critical challenges associated with privacy, security, and data integrity, specifically in infrastructures such as smart cities or smart manufacturing. Blockchain…
In modern healthcare systems, being able to share electronic health records is crucial for providing quality care and for enabling a larger spectrum of health services. Health data sharing is dependent on obtaining individual consent which,…
Deep learning, when integrated with a large amount of training data, has the potential to outperform machine learning in terms of high accuracy. Recently, privacy-preserving deep learning has drawn significant attention of the research…
Machine learning models offer the capability to forecast future energy production or consumption and infer essential unknown variables from existing data. However, legal and policy constraints within specific energy sectors render the data…
Recent advances in blockchain technologies have provided exciting opportunities for decentralized applications. Specifically, blockchain-based smart contracts enable credible transactions without authorized third parties. The attractive…
Financial fraud cases are on the rise even with the current technological advancements. Due to the lack of inter-organization synergy and because of privacy concerns, authentic financial transaction data is rarely available. On the other…
Sensitive records stored in the cloud such as healthcare records, private conversation and credit card information are targets of hackers and privacy abuse. Current information and record management systems have difficulties achieving…
This paper critically reviews the integration of Artificial Intelligence (AI) and blockchain technologies in the context of Medical Internet of Things (MedIoT) applications, where they collectively promise to revolutionize healthcare…
The safety-critical scenarios of artificial intelligence (AI), such as autonomous driving, Internet of Things, smart healthcare, etc., have raised critical requirements of trustworthy AI to guarantee the privacy and security with reliable…
The healthcare industry has witnessed significant transformations in e-health services by using mobile edge computing (MEC) and blockchain to facilitate healthcare operations. Many MEC-blockchain-based schemes have been proposed, but some…
Internet of Things (IoT) services necessitate the storage, transmission, and analysis of diverse data for inference, autonomy, and control. Blockchains, with their inherent properties of decentralization and security, offer efficient…
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.…
The proliferation of deep learning applications in healthcare calls for data aggregation across various institutions, a practice often associated with significant privacy concerns. This concern intensifies in medical image analysis, where…
Federated machine learning (FL) allows to collectively train models on sensitive data as only the clients' models and not their training data need to be shared. However, despite the attention that research on FL has drawn, the concept still…