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 …

Collaborative graph analysis across multiple institutions is becoming increasingly popular.
Realistic examples include social network analysis across various social platforms, financial …

In recent years, decentralized social networks have gained increasing attention, where each
client maintains a local view of a social graph. To provide services based on graph learning …

Graph data has been widely applied due to its powerful expressive capabilities. The release
of raw graph data without preprocessing may lead to privacy information leakage. Thus …

Triangle count estimation is a fundamental task in federated graph analysis. Yet, directly
collecting local counts from users exposes individuals to severe privacy risks, as the local …

As the implementation of machine learning (ML) systems becomes more widespread,
especially with the introduction of larger ML models, we perceive a spring demand for …

Streaming graphs are ubiquitous in daily life, such as evolving social networks and dynamic
communication systems. Due to the sensitive information contained in the graph, directly …

With the ever-increasing intertwining of social networks and daily existence, the
accumulation of personal privacy information is steadily mounting. However, the exposure of …

Differentially private graph analysis is a powerful tool for deriving insights from diverse graph
data while protecting individual information. Designing private analytic algorithms for …