The application of graph analytics to various domains has yielded tremendous societal and
economical benefits in recent years. However, the increasingly widespread adoption of …

We develop and study multicalibration as a new measure of fairness in machine learning
that aims to mitigate inadvertent or malicious discrimination that is introduced at training time …

The problem of privacy-preserving data analysis has a long history spanning multiple
disciplines. As electronic data about individuals becomes increasingly detailed, and as …

Differential privacy is an essential and prevalent privacy model that has been widely
explored in recent decades. This survey provides a comprehensive and structured overview …

Interactive querying of a database degrades the privacy level. In this paper we answer the
fundamental question of characterizing the level of privacy degradation as a function of the …

Differential privacy provides strong privacy preservation guarantee in information sharing.
As social network analysis has been enjoying many applications, it opens a new arena for …

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 …

In this article, we demonstrate that, ignoring computational constraints, it is possible to
release synthetic databases that are useful for accurately answering large classes of queries …

With the increasing prevalence of information networks, research on privacy-preserving
network data publishing has received substantial attention recently. There are two streams …

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 …