Internet of Things (IoT) devices have brought much efficiency and convenience to our daily
life. However, the devices may collect a myriad of data from people without their consent …

As our physical environments become ever-more connected, instrumented, and automated,
it can be increasingly difficult for users to understand what is happening within them and …

Data provenance describes how data came to be in its present form. It includes data sources
and the transformations that have been applied to them. Data provenance has many uses …

Federated learning (FL) can enable distributed model training over mobile nodes without
sharing privacy-sensitive raw data. However, to achieve efficient FL, one significant …

The era of pervasive computing has resulted in countless devices that continuously monitor
users and their environment, generating an abundance of user behavioural data. Such data …

Identifying the root cause and impact of a system intrusion remains a foundational challenge
in computer security. Digital provenance provides a detailed history of the flow of information …

Demand is growing for more accountability regarding the technological systems that
increasingly occupy our world. However, the complexity of many of these systems—often …

Internet of Things (IoT) is proposed and used in diverse application domains. In IoT, nodes
commonly have a low capacity to maintain security on their own expenses, which increases …

The study aims at discussing the security of medical data in the Internet era. By using k-
anonymity (KA) and differential privacy (DP), an algorithm model combining KA and DP was …

The physical, ubiquitous, and autonomous nature of the emerging Internet of Things (IoT)
raises various accountability challenges relating to safety and security, privacy and …