Machine Learning (ML) models are ubiquitous in real world applications and are a constant
focus of research. At the same time, the community has started to realize the importance of …

Since its introduction in 2006, differential privacy has emerged as a predominant statistical
tool for quantifying data privacy in academic works. Yet despite the plethora of research and …

Recent years have witnessed the adoption of differential privacy (DP) in practical database
systems like PINQ, FLEX, and PrivateSQL. Such systems allow data analysts to query …

We study the problem of multi-task learning under user-level differential privacy, in which n
users contribute data to m tasks, each involving a subset of users. One important aspect of …

We design, to the best of our knowledge, the first differentially private (DP) stream
aggregation processing system at scale. Our system--Differential Privacy SQL Pipelines (DP …

Differentially private computation often begins with a bound on some $ d $-dimensional
statistic's $\ell_p $ sensitivity. For pure differential privacy, the $ K $-norm mechanism can …

In this article, we present a detailed review of current practices and state-of-the-art
methodologies in the field of differential privacy (DP), with a focus of advancing DP's …

Differential privacy is the gold standard for statistical data release. Used by governments,
companies, and academics, its mathematically rigorous guarantees and worst-case …

Partition selection, or set union, is an important primitive in differentially private mechanism
design: in a database where each user contributes a list of items, the goal is to publish as …

There has been an explosion of research on differential privacy (DP) and its various
applications in recent years, ranging from novel variants and accounting techniques in …