Prior work in differential privacy has produced techniques for answering aggregate queries
over sensitive data in a privacy-preserving way. These techniques achieve privacy by …

We give a new proof of the" transfer theorem" underlying adaptive data analysis: that any
mechanism for answering adaptively chosen statistical queries that is differentially private …

We study the problem of publishing a stream of real-valued data satisfying differential
privacy (DP). One major challenge is that the maximal possible value in the stream can be …

Differential Privacy (DP) has received increasing attention as a rigorous privacy framework.
Many existing studies employ traditional DP mechanisms (eg, the Laplace mechanism) as …

Differential privacy is a precise mathematical constraint meant to ensure privacy of individual
pieces of information in a database even while queries are being answered about the …

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: A Primer for a Non-Technical Audience Page 1 Differential Privacy: A
Primer for a Non-Technical Audience Alexandra Wood, Micah Altman, Aaron Bembenek, Mark …

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

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 …

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 …