Differential privacy is widely accepted as the de facto method for preventing data leakage in
ML, and conventional wisdom suggests that it offers strong protection against privacy …

In this paper, we aim to understand the generalization properties of generative adversarial
networks (GANs) from a new perspective of privacy protection. Theoretically, we prove that a …

Differentially Private methods for training Deep Neural Networks (DNNs) have progressed
recently, in particular with the use of massive batches and aggregated data augmentations …

The Internet of Things (IoT) is transforming major industries, including but not limited to
healthcare, agriculture, finance, energy, and transportation. IoT platforms are continually …

This paper presents LDP-Fed, a novel federated learning system with a formal privacy
guarantee using local differential privacy (LDP). Existing LDP protocols are developed …

In the recent decade, deep learning techniques have been widely adopted for founding
artificial Intelligent applications, which led to successes in many data analysis tasks, such as …

With the advent of the era of big data, deep learning has become a prevalent building block
in a variety of machine learning or data mining tasks, such as signal processing, network …

Deep Learning (DL) is a powerful solution for complex problems in many disciplines such as
finance, medical research, or social sciences. Due to the high computational cost of DL …

Recent advances in machine learning have largely benefited from the massive accessible
training data. However, large-scale data sharing has raised great privacy concerns. In this …

Given access to a machine learning model, can an adversary reconstruct the model's
training data? This work studies this question from the lens of a powerful informed adversary …