With the rapid development of low-cost consumer electronics and pervasive adoption of next
generation wireless communication technologies, a tremendous amount of data has been …

Machine unlearning is a process of removing the impact of some training data from the
machine learning (ML) models upon receiving removal requests. While straightforward and …

Differential privacy has been a de facto privacy standard in defining privacy and handling
privacy preservation. It has had great success in scenarios of local data privacy and …

Publishing trajectory data (individual's movement information) is very useful, but it also
raises privacy concerns. To handle the privacy concern, in this paper, we apply differential …

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 …

Vertical Federated Learning (FL) is a new paradigm that enables users with non-
overlapping attributes of the same data samples to jointly train a model without directly …

We develop the first language-based, Privacy by Design approach that provides support for
a rich class of privacy policies. The policies are user-defined, rather than programmer …

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 …

Large Language Models (LLMs) are gaining increasing attention due to their exceptional
performance across numerous tasks. As a result, the general public utilize them as an …

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 …