Differential privacy has been a de facto privacy standard in defining privacy and handling
privacy preservation. It has had great success in scenarios of local data privacy and …

Nowadays, many computer and communication systems generate graph data. Graph data
span many different domains, ranging from online social network data from networks like …

Deep Learning has recently become hugely popular in machine learning for its ability to
solve end-to-end learning systems, in which the features and the classifiers are learned …

High-dimensional crowdsourced data collected from numerous users produces rich
knowledge about our society; however, it also brings unprecedented privacy threats to the …

In this paper, we focus on develo** a novel mechanism to preserve differential privacy in
deep neural networks, such that:(1) The privacy budget consumption is totally independent …

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 …

Shortly after it was first introduced in 2006, differential privacy became the flagship data
privacy definition. Since then, numerous variants and extensions were proposed to adapt it …

We survey the literature on the privacy of trajectory micro-data, ie, spatiotemporal
information about the mobility of individuals, whose collection is becoming increasingly …

The increasing popularity of social media has attracted a huge number of people to
participate in numerous activities on a daily basis. This results in tremendous amounts of …

Many modern databases include personal and sensitive correlated data, such as private
information on users connected together in a social network, and measurements of physical …