Abstract Machine Learning (ML) models are ubiquitous in real-world applications and are a
constant focus of research. Modern ML models have become more complex, deeper, and …

Machine learning (ML) models have been widely applied to various applications, including
image classification, text generation, audio recognition, and graph data analysis. However …

A membership inference attack allows an adversary to query a trained machine learning
model to predict whether or not a particular example was contained in the model's training …

How much does a machine learning algorithm leak about its training data, and why?
Membership inference attacks are used as an auditing tool to quantify this leakage. In this …

Membership inference is one of the simplest privacy threats faced by machine learning
models that are trained on private sensitive data. In this attack, an adversary infers whether a …

Data-driven machine learning (ML) has emerged as a promising approach for building
accurate and robust statistical models from medical data, which is collected in huge volumes …

Diffusion-based generative models have shown great potential for image synthesis, but
there is a lack of research on the security and privacy risks they may pose. In this paper, we …

To prevent unintentional data leakage, research community has resorted to data generators
that can produce differentially private data for model training. However, for the sake of the …

We propose and implement a Privacy-preserving Federated Learning (PPFL) framework for
mobile systems to limit privacy leakages in federated learning. Leveraging the widespread …

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 …