Abstract Machine Learning (ML) algorithms are used to train the machines to perform
various complicated tasks that begin to modify and improve with experiences. It has become …

Federated learning (FL), as a distributed machine learning paradigm, promotes personal
privacy by local data processing at each client. However, relying on a centralized server for …

Electronic health record (EHR) management systems require the adoption of effective
technologies when health information is being exchanged. Current management …

We introduce PyVertical, a framework supporting vertical federated learning using split
neural networks. The proposed framework allows a data scientist to train neural networks on …

This paper proposed a novel privacy model for Electronic Health Records (EHR) systems
utilizing a conceptual privacy ontology and Machine Learning (ML) methodologies. It …

We describe a threat model under which a split network-based federated learning system is
susceptible to a model inversion attack by a malicious computational server. We …

A common privacy issue in traditional machine learning is that data needs to be disclosed
for the training procedures. In situations with highly sensitive data such as healthcare …

Clinical trials and drug discovery would not be effective without the collaboration of
institutions. Earlier, it has been at the cost of individual's privacy. Several pacts and …

Data uploading needs security and privacy in the cloud. But there are some problems like
centralized provenance data (PD) collection, storage, lack of security, integrity, and more …

This paper presents a systematic review of the literature on evaluation criteria for
Trustworthy Artificial Intelligence (TAI), with a focus on the seven EU principles of TAI. This …