It is of crucial importance to simultaneously protect against sensitive attributes in data while
building predictive models. In this paper, we tackle the problem of learning representations …

Generative Adversarial Network (GAN) and its variants serve as a perfect representation of
the data generation model, providing researchers with a large amount of high-quality …

There are several shortcomings in current methods of generating synthetic data using
Generative Adversarial Networks (GANs). First, they tend to only emulate certain attributes of …

Data privacy has emerged as an important issue as data-driven deep learning has been an
essential component of modern machine learning systems. For instance, there could be a …

Recent advances in computing have allowed for the possibility to collect large amounts of
data on personal activities and private living spaces. Collecting and publishing a dataset in …

Adversarial learning is commonly used to extract latent data representations which are
expressive to predict the target attribute but indistinguishable in the privacy attribute …

Network simulation is essential to test adversarial search problems for privacy preservation
and benchmarking purposes. Different generative models have been developed for single …

The remarkable success of machine learning, especially deep learning, has produced a
variety of cloud-based services for mobile users. Such services require an end user to send …

We present a framework to learn privacy-preserving encodings of images that inhibit
inference of chosen private attributes, while allowing recovery of other desirable information …

We present a data-driven framework called generative adversarial privacy (GAP). Inspired
by recent advancements in generative adversarial networks (GANs), GAP allows the data …