Artificial Intelligence (AI) has attracted a great deal of attention in recent years. However,
alongside all its advancements, problems have also emerged, such as privacy violations …

We present a rigorous methodology for auditing differentially private machine learning by
adding multiple carefully designed examples called canaries. We take a first principles …

We present DP-Sniper, a practical black-box method that automatically finds violations of
differential privacy. DP-Sniper is based on two key ideas:(i) training a classifier to predict if …

We initiate a systematic study of sublinear algorithms for approximately testing properties of
real-valued data with respect to L p distances for p= 1, 2. Such algorithms distinguish …

We consider the problem of property testing for differential privacy: with black-box access to
a purportedly private algorithm, can we verify its privacy guarantees? In particular, we show …

We show how the communication complexity method introduced in (Blais, Brody, Matulef
2012) can be used to prove lower bounds on the number of queries required to test …

New regulations and increased awareness of data privacy have led to the deployment of
new and more efficient differentially private mechanisms across both public institutions and …

The online manipulation-resilient testing model, proposed by Kalemaj, Raskhodnikova and
Varma (ITCS 2022 and Theory of Computing 2023), studies property testing in situations …

In today's data-driven world, programmers routinely incorporate user data into complex
algorithms, heuristics, and application pipelines. While often beneficial, this practice can …

The primary problem in property testing is to decide whether a given function satisfies a
certain property or is far from any function satisfying it. This crucially requires a notion of …