Differentially private continual releases of streaming frequency moment estimations
The streaming model of computation is a popular approach for working with large-scale
data. In this setting, there is a stream of items and the goal is to compute the desired …
data. In this setting, there is a stream of items and the goal is to compute the desired …
On differential privacy and adaptive data analysis with bounded space
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 …
data analysis. Specifically, Under standard cryptographic assumptions, we show that there …
Order-invariant cardinality estimators are differentially private
We consider privacy in the context of streaming algorithms for cardinality estimation. We
show that a large class of algorithms all satisfy $\epsilon $-differential privacy, so long as (a) …
show that a large class of algorithms all satisfy $\epsilon $-differential privacy, so long as (a) …
How to make your approximation algorithm private: A black-box differentially-private transformation for tunable approximation algorithms of functions with low …
We develop a framework for efficiently transforming certain approximation algorithms into
differentially-private variants, in a black-box manner. Specifically, our results focus on …
differentially-private variants, in a black-box manner. Specifically, our results focus on …
Additive noise mechanisms for making randomized approximation algorithms differentially private
J Tětek - arXiv preprint arXiv:2211.03695, 2022 - arxiv.org
The exponential increase in the amount of available data makes taking advantage of them
without violating users' privacy one of the fundamental problems of computer science. This …
without violating users' privacy one of the fundamental problems of computer science. This …
Private data stream analysis for universal symmetric norm estimation
We study how to release summary statistics on a data stream subject to the constraint of
differential privacy. In particular, we focus on releasing the family of symmetric norms, which …
differential privacy. In particular, we focus on releasing the family of symmetric norms, which …
Secure federated correlation test and entropy estimation
We propose the first federated correlation test framework compatible with secure
aggregation, namely FED-$\chi^ 2$. In our protocol, the statistical computations are recast …
aggregation, namely FED-$\chi^ 2$. In our protocol, the statistical computations are recast …
Counting distinct elements in the turnstile model with differential privacy under continual observation
Privacy is a central challenge for systems that learn from sensitive data sets, especially
when a system's outputs must be continuously updated to reflect changing data. We …
when a system's outputs must be continuously updated to reflect changing data. We …
Differentially Private Histogram, Predecessor, and Set Cardinality under Continual Observation
M Henzinger, AR Sricharan, TA Steiner - arXiv preprint arXiv:2306.10428, 2023 - arxiv.org
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 …
differential privacy considers the data to be static. The dynamic setting, called differential …
DPSW-Sketch: A Differentially Private Sketch Framework for Frequency Estimation over Sliding Windows (Technical Report)
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 …
arriving in the form of a stream, and only the most recent $ w $ items are used for analysis. In …