Huge amounts of unstructured data including image, video, audio, and text are ubiquitously
generated and shared, and it is a challenge to protect sensitive personal information in …

Corporations, organizations, and governments have collected, digitized, and stored
information in digital forms since the invention of computers, and the speed of such data …

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 …

Since big data becomes a main impetus to the next generation of IT industry, data privacy
has received considerable attention in recent years. To deal with the privacy challenges …

Differential privacy is a popular privacy model within the research community because of the
strong privacy guarantee it offers, namely that the presence or absence of any individual in a …

Anonymizing private data before release is not enough to reliably protect privacy, as Netflix
and AOL have learned to their cost. Recent research on differential privacy opens a way to …

Computing aggregate statistics about user data is of vital importance for a variety of services
and systems, but this practice has been shown to seriously undermine the privacy of users …

Privacy preserving on data mining and data release has attracted an increasing research
interest over a number of decades. Differential privacy is one influential privacy notion that …

Differential privacy is widely accepted as a powerful framework for providing strong, formal
privacy guarantees for aggregate data analysis. A limitation of the model is that the same …

Since its conception in 2006, differential privacy has emerged as the de-facto standard in
data privacy, owing to its robust mathematical guarantees, generalised applicability and rich …