Data confidentiality is a central concern in modern computer systems and services, as
sensitive data from users and companies are being increasingly delegated to such systems …

A lack of trust in the providers is still a major barrier to cloud computing adoption–especially
when sensitive data is involved. While current privacy-enhancing technologies, such as …

The “Right to be Forgotten” rule in machine learning (ML) practice enables some individual
data to be deleted from a trained model, as pursued by recently developed machine …

In end-to-end encrypted (E2EE) messaging systems, protecting communication metadata,
such as who is communicating with whom, at what time, etc., remains a challenging …

Confidentiality, integrity protection, and high availability, abbreviated to CIA, are essential
properties for trustworthy data systems. The rise of cloud computing and the growing …

Protection of personal data has been raised to be among the top requirements of modern
systems. At the same time, it is now frequent that the owner of the data and the owner of the …

Data processing on non-trusted infrastructures, such as the public cloud, has become
increasingly popular, despite posing risks to data privacy. However, the existing cloud …

As an essential technology underpinning trusted computing, the trusted execution
environment (TEE) allows one to launch computation tasks on both on-and off-premises …

The decisions involved in choosing technology components for systems are poorly
understood. This is especially so where the choices pertain to system security and …

Federated Learning (FL) is an emerging machine learning paradigm that enables multiple
clients to jointly train a model to take benefits from diverse datasets from the clients without …