Zobrazeno 1 - 6
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pro vyhledávání: '"Kazan, Zeki"'
Autor:
Kazan, Zeki, Reiter, Jerome P.
We describe Bayesian inference for the parameters of Gaussian models of bounded data protected by differential privacy. Using this setting, we demonstrate that analysts can and should take constraints imposed by the bounds into account when specifyin
Externí odkaz:
http://arxiv.org/abs/2405.13801
Autor:
Kazan, Zeki, Reiter, Jerome P.
When releasing outputs from confidential data, agencies need to balance the analytical usefulness of the released data with the obligation to protect data subjects' confidentiality. For releases satisfying differential privacy, this balance is reflec
Externí odkaz:
http://arxiv.org/abs/2306.13214
We present a generic framework for creating differentially private versions of any hypothesis test in a black-box way. We analyze the resulting tests analytically and experimentally. Most crucially, we show good practical performance for small data s
Externí odkaz:
http://arxiv.org/abs/2302.04260
Autor:
Kazan, Zeki, Reiter, Jerome
We propose Bayesian methods to assess the statistical disclosure risk of data released under zero-concentrated differential privacy, focusing on settings with a strong hierarchical structure and categorical variables with many levels. Risk assessment
Externí odkaz:
http://arxiv.org/abs/2204.04253
Hypothesis tests are a crucial statistical tool for data mining and are the workhorse of scientific research in many fields. Here we study differentially private tests of independence between a categorical and a continuous variable. We take as our st
Externí odkaz:
http://arxiv.org/abs/1903.09364
Hypothesis tests are a crucial statistical tool for data mining and are the workhorse of scientific research in many fields. Here we present a differentially private analogue of the classic Wilcoxon signed-rank hypothesis test, which is used when com
Externí odkaz:
http://arxiv.org/abs/1809.01635