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 …

Releasing representative data sets without compromising the data privacy has attracted
increasing attention from the database community in recent years. Differential privacy is an …

Privacy-preserving data publishing is an important problem that has been the focus of
extensive study. The state-of-the-art solution for this problem is differential privacy, which …

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 …

Differential privacy (DP) is currently the de-facto standard for achieving privacy in data
analysis, which is typically implemented either in the" central" or" local" model. The local …

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 …

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

The objective of differential privacy (DP) is to protect privacy by producing an output
distribution that is indistinguishable between any two neighboring databases. However …

In this paper, we study the problem of publishing high-dimensional data in a distributed multi-
party environment under differential privacy. In particular, with the assistance of a semi …

Differential privacy has recently emerged in private statistical aggregate analysis as one of
the strongest privacy guarantees. A limitation of the model is that it provides the same …