Abstract Online Social Networks are perpetually evolving and used in plenteous
applications such as content sharing, chatting, making friends/followers, customer …

As machine learning becomes more widely used, the need to study its implications in
security and privacy becomes more urgent. Although the body of work in privacy has been …

Deep neural networks (DNNs) have demonstrated their superiority in practice. Arguably, the
rapid development of DNNs is largely benefited from high-quality (open-sourced) datasets …

In a membership inference attack, an attacker aims to infer whether a data sample is in a
target classifier's training dataset or not. Specifically, given a black-box access to the target …

Recommender systems (RS), serving at the forefront of Human-centered AI, are widely
deployed in almost every corner of the web and facilitate the human decision-making …

Recent years have seen a proliferation of research on adversarial machine learning.
Numerous papers demonstrate powerful algorithmic attacks against a wide variety of …

Recommender system is an important component of many web services to help users locate
items that match their interests. Several studies showed that recommender systems are …

Graph-based semi-supervised node classification (GraphSSC) has wide applications,
ranging from networking and security to data mining and machine learning, etc. However …

Smart IoT systems can integrate knowledge from the surrounding environment, and they are
critical components of the next-generation Internet. Such systems usually collect data from …

With the advance of language models, privacy protection is receiving more attention.
Training data extraction is therefore of great importance, as it can serve as a potential tool to …