The advancement of data communication technologies promotes widespread data collection
and transmission in various application domains, thereby expanding big data significantly …

We propose a general approach for differentially private synthetic data generation, that
consists of three steps:(1) select a collection of low-dimensional marginals,(2) measure …

By learning generative models of semantic-rich data distributions from samples, generative
adversarial network (GAN) has recently attracted intensive research interests due to its …

Differential privacy provides strong privacy preservation guarantee in information sharing.
As social network analysis has been enjoying many applications, it opens a new arena for …

Mobile edge computing helps healthcare Internet of Things (IoT) devices with energy
harvesting provide satisfactory quality of experiences for computation intensive applications …

We propose AIM, a new algorithm for differentially private synthetic data generation. AIM is a
workload-adaptive algorithm within the paradigm of algorithms that first selects a set of …

As the prevalence of data analysis grows, safeguarding data privacy has become a
paramount concern. Consequently, there has been an upsurge in the development of …

With rapid development of e-healthcare systems, patients that are equipped with resource-
limited e-healthcare devices (Internet of Things) generate huge amount of health data for …

Marginal tables are the workhorse of capturing the correlations among a set of attributes. We
consider the problem of constructing marginal tables given a set of user's multi-dimensional …

Constructing surrogate models for uncertainty quantification (UQ) on complex partial
differential equations (PDEs) having inherently high-dimensional O (10 n), n≥ 2, stochastic …