(ε, δ) differential privacy has seen increased adoption recently, especially in private machine
learning applications. While this privacy definition allows provably limiting the amount of …

We propose CheckDP, an automated and integrated approach for proving or disproving
claims that a mechanism is differentially private. CheckDP can find counterexamples for …

Differential privacy has become a de facto standard for releasing data in a privacy-
preserving way. Creating a differentially private algorithm is a process that often starts with a …

We study the complexity of the problem of verifying differential privacy for while-like
programs working over boolean values and making probabilistic choices. Programs in this …

Differential privacy is a mathematical framework for developing statistical computations with
provable guarantees of privacy and accuracy. In contrast to the privacy component of …

We present new methods for assessing the privacy guarantees of an algorithm with regard
to Rényi Differential Privacy. To the best of our knowledge, this work is the first to address …

We consider the problem of checking the differential privacy of online randomized
algorithms that process a stream of inputs and produce outputs corresponding to each input …

In this work, we introduce a new approach for statistical quantification of differential privacy
in a black box setting. We present estimators and confidence intervals for the optimal privacy …

Differential privacy is a de facto standard in data privacy with applications in the private and
public sectors. Most of the techniques that achieve differential privacy are based on a …

Language support for differentially private programming is both crucial and delicate. While
elaborate program logics can be very expressive, type-system-based approaches using …