Machine learning heavily relies on data, but real-world applications often encounter various
data-related issues. These include data of poor quality, insufficient data points leading to …

Although highly valuable for a variety of applications, urban mobility data are rarely made
openly available, as it contains sensitive personal information. Synthetic data aims to solve …

Trajectory data has the potential to greatly benefit a wide-range of real-world applications,
such as tracking the spread of the disease through people's movement patterns and …

The generation of trajectory data has increased dramatically with the advent and
widespread use of GPS-enabled devices. This rich source of data provides invaluable …

Few-shot-based facial recognition systems have gained increasing attention due to their
scalability and ability to work with a few face images during the model deployment phase …

Mobility data captures the locations of moving objects such as humans, animals, and cars.
With the availability of GPS-equipped mobile devices and other inexpensive location …

Graph data is used in a wide range of applications, while analyzing graph data without
protection is prone to privacy breach risks. To mitigate the privacy risks, we resort to the …

As the utilization of network traces for the network measurement research becomes
increasingly prevalent, concerns regarding privacy leakage from network traces have …

Data is a critical asset in AI, as high-quality datasets can significantly improve the
performance of machine learning models. In safety-critical domains such as autonomous …

Recommender systems predict and suggest relevant options to users in various domains,
such as e-commerce, streaming services, and social media. Recently, deep reinforcement …