54 citations · 74 across the 10 of their papers we have counts for
10 papers
Privacy-preserving Inference of Group Mean Difference in Zero-inflated Right Skewed Data with Partitioning and Censoring
Fang Liu, Ruyu Zhou, Yiming Paul Li +2
We examine privacy-preserving inferences of group mean differences in zero-inflated right-skewed (zirs) data. Zero inflation and right skewness are typical characteristics of ads c…
AdaAnn: Adaptive Annealing Scheduler for Probability Density Approximation
Emma R. Cobian, Jonathan D. Hauenstein, Fang Liu +1
Approximating probability distributions can be a challenging task, particularly when they are supported over regions of high geometrical complexity or exhibit multiple modes. Annea…
Disclosure Risk from Homogeneity Attack in Differentially Private Frequency Distribution
Fang Liu, Xingyuan Zhao
Differential privacy (DP) provides a robust model to achieve privacy guarantees for released information. We examine the protection potency of sanitized multi-dimensional frequency…
A Statistical Overview on Data Privacy
Fang Liu
The eruption of big data with the increasing collection and processing of vast volumes and variety of data have led to breakthrough discoveries and innovation in science, engineeri…
Construction of Differentially Private Empirical Distributions from a low-order Marginals Set through Solving Linear Equations with l2 Regularization
Evercita C. Eugenio, Fang Liu
We introduce a new algorithm, Construction of dIfferentially Private Empirical Distributions from a low-order marginals set tHrough solving linear Equations with l2 Regularization…
Bayesian Hierarchical Spatial Model for Small Area Estimation with Non-ignorable Nonresponses and Its Applications to the NHANES Dental Caries Assessments
Ick Hoon Jin, Fang Liu, Evercita C. Eugenio +2
The National Health and Nutrition Examination Survey (NHANES) is a major program of the National Center for Health Statistics, designed to assess the health and nutritional status…
Differentially Private Data Release via Statistical Election to Partition Sequentially
Claire McKay Bowen, Fang Liu, Binyue Su
Differential Privacy (DP) formalizes privacy in mathematical terms and provides a robust concept for privacy protection. DIfferentially Private Data Synthesis (DIPS) techniques pro…
Statistical Properties of Sanitized Results from Differentially Private Laplace Mechanism with Univariate Bounding Constraints
Fang Liu
Protection of individual privacy is a common concern when releasing and sharing data and information. Differential privacy (DP) formalizes privacy in probabilistic terms without ma…
Model-based Differentially Private Data Synthesis and Statistical Inference in Multiply Synthetic Differentially Private Data
Fang Liu
We propose the approach of model-based differentially private synthesis (modips) in the Bayesian framework for releasing individual-level surrogate/synthetic datasets with privacy…
Comparative Study of Differentially Private Data Synthesis Methods
Claire McKay Bowen, Fang Liu
When sharing data among researchers or releasing data for public use, there is a risk of exposing sensitive information of individuals in the data set. Data synthesis (DS) is a sta…