Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

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 learning algorithms, when applied to sensitive data, pose a distinct threat to
privacy. A growing body of prior work demonstrates that models produced by these …

Sensitive statistics are often collected across sets of users, with repeated collection of
reports done over time. For example, trends in users' private preferences or software usage …

We quantitatively investigate how machine learning models leak information about the
individual data records on which they were trained. We focus on the basic membership …

Generative models estimate the underlying distribution of a dataset to generate realistic
samples according to that distribution. In this paper, we present the first membership …

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 …

Today's systems produce a rapidly exploding amount of data, and the data further derives
more data, forming a complex data propagation network that we call the data's lineage …

The unprecedented success of deep learning is largely dependent on the availability of
massive amount of training data. In many cases, these data are crowd-sourced and may …

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 …