We study the accuracy of differentially private mechanisms in the continual release model. A
continual release mechanism receives a sensitive dataset as a stream of $ T $ inputs and …

We investigate the adversarial robustness of streaming algorithms. In this context, an
algorithm is considered robust if its performance guarantees hold even if the stream is …

In the adversarially robust streaming model, a stream of elements is presented to an
algorithm and is allowed to depend on the output of the algorithm at earlier times during the …

Laws of large numbers guarantee that given a large enough sample from some population,
the measure of any fixed sub-population is well-estimated by its frequency in the sample. We …

Given an input that undergoes a sequence of updates, a dynamic algorithm maintains a
valid solution to some predefined problem at any point in time; the goal is to design an …

Robustness against adversarial attacks has recently been at the forefront of algorithmic
design for machine learning tasks. In the adversarial streaming model, an adversary gives …

Classical streaming algorithms operate under the (not always reasonable) assumption that
the input stream is fixed in advance. Recently, there is a growing interest in designing robust …

There has been a flurry of recent literature studying streaming algorithms for which the input
stream is chosen adaptively by a black-box adversary who observes the output of the …

A streaming algorithm is considered to be adversarially robust if it provides correct outputs
with high probability even when the stream updates are chosen by an adversary who may …

A streaming algorithm is adversarially robust if it is guaranteed to perform correctly even in
the presence of an adaptive adversary. The development and analysis of such algorithms …