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 …

We propose, implement, and evaluate a new algo-rithm for releasing answers to very large
numbersof statistical queries likek-way marginals, sub-ject to differential privacy. Our …

Many privacy mechanisms reveal high-level information about a data distribution through
noisy measurements. It is common to use this information to estimate the answers to new …

In the past few years, differential privacy has become a standard concept in the area of
privacy. One of the most important problems in this field is to answer queries while …

We show new lower bounds on the sample complexity of (ε, δ)-differentially private
algorithms that accurately answer large sets of counting queries. A counting query on a …

Many analysis and machine learning tasks require the availability of marginal statistics on
multidimensional datasets while providing strong privacy guarantees for the data subjects …

We consider the problem of publishing a differentially private synopsis of a d-dimensional
dataset so that one can reconstruct any k-way marginal contingency tables from the …

Multi-dimensional analytical (MDA) queries are often issued against a fact table with
predicates on (categorical or ordinal) dimensions and aggregations on one or more …

Suppose we would like to know all answers to a set of statistical queries C on a data set up
to small error, but we can only access the data itself using statistical queries. A trivial solution …

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 …