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pro vyhledávání: '"Gong, Ruobin"'
This work proposes a class of locally differentially private mechanisms for linear queries, in particular range queries, that leverages correlated input perturbation to simultaneously achieve unbiasedness, consistency, statistical transparency, and c
Externí odkaz:
http://arxiv.org/abs/2402.07066
Autor:
Bailie, James, Gong, Ruobin
Publikováno v:
International Journal of Approximate Reasoning 172 (2024)
Differential privacy (DP) is a class of mathematical standards for assessing the privacy provided by a data-release mechanism. This work concerns two important flavors of DP that are related yet conceptually distinct: pure $\varepsilon$-differential
Externí odkaz:
http://arxiv.org/abs/2401.15491
We propose new differential privacy solutions for when external \emph{invariants} and \emph{integer} constraints are simultaneously enforced on the data product. These requirements arise in real world applications of private data curation, including
Externí odkaz:
http://arxiv.org/abs/2212.00936
Differentially private mechanisms protect privacy by introducing additional randomness into the data. Restricting access to only the privatized data makes it challenging to perform valid statistical inference on parameters underlying the confidential
Externí odkaz:
http://arxiv.org/abs/2206.00710
This article proposes a set of categories, each one representing a particular distillation of important statistical ideas. Each category is labeled a "sense" because we think of these as essential in helping every statistical mind connect in construc
Externí odkaz:
http://arxiv.org/abs/2204.05313
Autor:
Caprio, Michele, Gong, Ruobin
We introduce dynamic probability kinematics (DPK), a method for an agent to mechanically update subjective beliefs in the presence of partial information. We then generalize DPK to dynamic imprecise probability kinematics (DIPK), which allows the age
Externí odkaz:
http://arxiv.org/abs/2110.04382
Many data applications have certain invariant constraints due to practical needs. Data curators who employ differential privacy need to respect such constraints on the sanitized data product as a primary utility requirement. Invariants challenge the
Externí odkaz:
http://arxiv.org/abs/2108.11527
Autor:
Gong, Ruobin, Meng, Xiao-Li
Differentially private data releases are often required to satisfy a set of external constraints that reflect the legal, ethical, and logical mandates to which the data curator is obligated. The enforcement of constraints, when treated as post-proces
Externí odkaz:
http://arxiv.org/abs/2008.10202
Autor:
Gong, Ruobin
Publikováno v:
Harvard Data Science Review, Special Issue 2, 2022
In a technical treatment, this article establishes the necessity of transparent privacy for drawing unbiased statistical inference for a wide range of scientific questions. Transparency is a distinct feature enjoyed by differential privacy: the proba
Externí odkaz:
http://arxiv.org/abs/2006.08522
The discrepant posterior phenomenon (DPP) is a counter-intuitive phenomenon that can frequently occur in a Bayesian analysis of multivariate parameters. It refers to the phenomenon that a parameter estimate based on a posterior is more extreme than b
Externí odkaz:
http://arxiv.org/abs/2001.08336