We study fine-grained error bounds for differentially private algorithms for counting under
continual observation. Our main insight is that the matrix mechanism when using lower …

Linear sketches have been widely adopted to process fast data streams, and they can be
used to accurately answer frequency estimation, approximate top K items, and summarize …

We study the space complexity of the two related fields of differential privacy and adaptive
data analysis. Specifically, Under standard cryptographic assumptions, we show that there …

The last few years have seen a surge of work on high dimensional statistics under privacy
constraints, mostly following two main lines of work: the" worst case" line, which does not …

This work studies the estimation of many statistical quantiles under differential privacy. More
precisely, given a distribution and access to iid samples from it, we study the estimation of …

Based on binary inquiries, we developed an algorithm to estimate population quantiles
under Local Differential Privacy (LDP). By self-normalizing, our algorithm provides …

In this work we consider the problem of differentially private computation ofquantiles for the
data, especially the highest quantiles such as maximum, butwith an unbounded range for …

Differential privacy (DP) is the state-of-the-art and rigorous notion of privacy for answering
aggregate database queries while preserving the privacy of sensitive information in the data …

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 …

Motivated by privacy concerns in long-term longitudinal studies in medical and social
science research, we study the problem of continually releasing differentially private …