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 …

Local differential privacy (LDP) is promising for private streaming data collection and
analysis. However, existing few LDP studies over streams either apply to finite streams only …

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 …

We study fine-grained error bounds for differentially private algorithms for counting under
continual observation. Our main insight is that the matrix mechanism when using lower …

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 …

Many applications rely on continual data collection to provide real-time information services,
eg, real-time road traffic forecasts. However, the collection of original data brings risks to …

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 …

In the foundational work of Dwork et al.[15] on continual observation under differential
privacy (DP), two privacy models have been proposed: event-level DP and user-level DP …

We study fine-grained error bounds for differentially private algorithms for averaging and
counting in the continual observation model. For this, we use the completely bounded …