activity
20182022
most citedA Statistical Overview on Data Privacy

7 citations · 7 across the 2 of their papers we have counts for

collaborators

5 papers

stat.CO2022

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…

cs.CR20207 cited

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…

cs.LG2018

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…

stat.AP2018

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…

stat.AP2018

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…