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

The massive generation, extensive sharing, and deep exploitation of data in the big data era
have raised unprecedented privacy threats. To address privacy concerns, various privacy …

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 …

Data privacy has been an important research topic in the security, theory and database
communities in the last few decades. However, many existing studies have restrictive …

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

Differential privacy has gained a lot of attention in recent years as a general model for the
protection of personal information when used and disclosed for secondary purposes. It has …

Differential privacy is a rigorous mathematical framework for evaluating and protecting data
privacy. In most existing studies, there is a vulnerable assumption that records in a dataset …

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 …

OpenData movement around the globe is demanding more access to information which lies
locked in public or private servers. As recently reported by a McKinsey publication, this data …

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 …