The introduction of online social networks (OSN) has transformed the way people connect
and interact with each other as well as share information. OSN have led to a tremendous …

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

Differential privacy promises to enable general data analytics while protecting individual
privacy, but existing differential privacy mechanisms do not support the wide variety of …

Differential privacy is an essential and prevalent privacy model that has been widely
explored in recent decades. This survey provides a comprehensive and structured overview …

Graph structured data have enabled several successful applications such as
recommendation systems and traffic prediction, given the rich node features and edges …

We develop algorithms for the private analysis of network data that provide accurate
analysis of realistic networks while satisfying stronger privacy guarantees than those of …

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 …

Given a set D of tuples defined on a domain Omega, we study differentially private
algorithms for constructing a histogram over Omega to approximate the tuple distribution in …

Graph data publishing under node-differential privacy (node-DP) is challenging due to the
huge sensitivity of queries. However, since a node in graph data oftentimes represents a …

Differentially private analysis of graphs is widely used for releasing statistics from sensitive
graphs while still preserving user privacy. Most existing algorithms however are in a …