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 …

Differential privacy has emerged as an important standard for privacy preserving
computation over databases containing sensitive information about individuals. Research …

Differential privacy is at a turning point. Implementations have been successfully leveraged
in private industry, the public sector, and academia in a wide variety of applications, allowing …

Over the last decade, differential privacy (DP) has emerged as the de facto standard privacy
notion for research in privacy-preserving data analysis and publishing. The DP notion offers …

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

When differential privacy was created more than a decade ago, the motivating example was
statistics published by an official statistics agency. In attempting to transition differential …

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 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 …

Differential privacy is becoming a gold standard notion of privacy; it offers a guaranteed
bound on loss of privacy due to release of query results, even under worst-case …

Differential privacy is a theoretical framework for ensuring the privacy of individual-level data
when performing statistical analysis of privacy-sensitive datasets. This tutorial provides an …