Motivated by the security risks of deep neural networks, such as various membership and
attribute inference attacks, differential privacy has emerged as a promising approach for …

Local differential privacy (LDP) is a prevalent measure of privacy protection as it provides
rigorous privacy guarantees and has been widely studied for statistical analysis, especially …

The wide deployment of machine learning algorithms has become a severe threat to user
data privacy. As the learning data is of high dimensionality and high orders, preserving its …

Releasing all pairwise shortest path (APSP) distances between vertices on general graphs
under weight Differential Privacy (DP) is known as a challenging task. In the previous …

We consider range queries on a graph under the constraints of differential privacy and query
ranges are defined as the set of edges on the shortest path of the graph. Edges in the graph …

Data aggregations enable privacy-aware data analytics for moving objects. A spatiotemporal
range count query is a fundamental query that aggregates the count of objects in a given …

Computational learning theory studies the design and analysis of learning algorithms, and it
is integral to the foundation of machine learning. In the modern era, classical computational …