Subgraph counting is fundamental for analyzing connection patterns or clustering
tendencies in graph data. Recent studies have applied LDP (Local Differential Privacy) to …

The popularity of federated learning comes from the possibility of better scalability and the
ability for participants to keep control of their data, improving data security and sovereignty …

In recent years, the shuffling model has been garnering attention in the realm of differential
privacy (DP). This study focuses on the fact that the shuffling model follows bounded DP …

Kemeny's constant for random walks on a graph is defined as the mean hitting time from one
node to another selected randomly according to the stationary distribution. It has found …

Graph Neural Networks (GNNs) have proven to be highly effective in addressing the link
prediction problem. However, the need for large amounts of user data to learn …

Differentially Private Federated Learning (DP-FL) has garnered attention as a collaborative
machine learning approach that ensures formal privacy. Most DP-FL approaches ensure DP …

For an undirected graph, its Kemeny's constant is defined as the mean hitting time of random
walks from one vertex to another chosen randomly according to the stationary distribution …

Star-join query is the fundamental task in data warehouse and has wide applications in On-
line Analytical Processing (olap) scenarios. Due to the large number of foreign key …

Data collection is indispensable for spatial crowd-sourcing services, such as resource
allocation, policymaking, and scientific explorations. However, privacy issues make it …

Local differential privacy (LDP) gives a strong privacy guarantee to be used in a distributed
setting like federated learning (FL). LDP mechanisms in FL protect a client's gradient by …