In this paper, we study streaming algorithms that minimize the number of changes made to
their internal state (ie, memory contents). While the design of streaming algorithms typically …

The task of finding Hierarchical Heavy Hitters (HHH) was introduced by Cormode et al.[12]
as a generalisation of the heavy hitter problem. While finding HHH in data streams has been …

The sliding window model of computation captures scenarios in which data are continually
arriving in the form of a stream, and only the most recent w items are used for analysis. In …

Consider the problem of securely identifying-heavy hitters, where given a set of client inputs,
the goal is to identify those inputs which are held by at least clients in a privacy-preserving …

The exponential increase in the amount of available data makes taking advantage of them
without violating users' privacy one of the fundamental problems of computer science. This …

We present a simple variant of the Gaussian mechanism for answering differentially private
queries when the sensitivity space has a certain common structure. Our motivating problem …

Differential privacy is the de-facto privacy standard in data analysis. The classic model of
differential privacy considers the data to be static. The dynamic setting, called differential …

Computing the quantile of a massive data stream has been a crucial task in networking and
data management. However, existing solutions assume a centralized model where one data …

Protecting sensitive information on data streams is a critical challenge for modern systems.
Current approaches to privacy in data streams follow two strategies. The first transforms the …

We consider the problem of clustering privately a dataset in $\mathbb {R}^ d $ that
undergoes both insertion and deletion of points. Specifically, we give an $\varepsilon …