Information about us, our actions, and our preferences is created at scale through surveys or
scientific studies or as a result of our interaction with digital devices such as smartphones …

The use of synthetic data is a promising solution to facilitate the sharing and reuse of health-
related data beyond its initial collection while addressing privacy concerns. However, there …

Synthetic data is seen as the most promising solution to share individual-level data while
preserving privacy. Shadow modeling-based Membership Inference Attacks (MIAs) have …

Real-world data often exhibits bias, imbalance, and privacy risks. Synthetic datasets have
emerged to address these issues by enabling a paradigm that relies on generative AI …

Training generative models to produce synthetic data is meant to provide a privacy-friendly
approach to data release. However, we get robust guarantees only when models are trained …

Preserving individual privacy while enabling collaborative data sharing is crucial for
organizations. Synthetic data generation is one solution, producing artificial data that mirrors …

The availability of rich and vast data sources has greatly advanced machine learning
applications in various domains. However, data with privacy concerns comes with stringent …

Location data is frequently collected from populations and shared in aggregate form to guide
policy and decision making. However, the prevalence of aggregated data also raises the …

As the utilization of network traces for the network measurement research becomes
increasingly prevalent, concerns regarding privacy leakage from network traces have …

While there were already some privacy challenges organized in the domain of data
sanitization, they have mainly focused on the defense side of the problem. To favor the …