45 citations · 47 across the 5 of their papers we have counts for
6 papers
Differentially Private Linear Regression and Synthetic Data Generation with Statistical Guarantees
Shurong Lin, Aleksandra Slavković, Deekshith Reddy Bhoomireddy
In the social sciences, small- to medium-scale datasets are common, and linear regression is canonical. In privacy-aware settings, much work has focused on differentially private (…
Statistics-Friendly Confidentiality Protection for Establishment Data, with Applications to the QCEW
Kaitlyn Webb, Prottay Protivash, John Durrell +3
Confidentiality for business data is an understudied area of disclosure avoidance, where legacy methods struggle to provide acceptable results. Standard formal privacy techniques f…
Differentially Private Synthetic Heavy-tailed Data
Tran Tran, Matthew Reimherr, Aleksandra Slavković
The U.S. Census Longitudinal Business Database (LBD) product contains employment and payroll information of all U.S. establishments and firms dating back to 1976 and is an invaluab…
A Latent Class Modeling Approach for Generating Synthetic Data and Making Posterior Inferences from Differentially Private Counts
Michelle Pistner Nixon, Andrés F. Barrientos, Jerome P. Reiter +1
Several algorithms exist for creating differentially private counts from contingency tables, such as two-way or three-way marginal counts. The resulting noisy counts generally do n…
Privacy for Spatial Point Process Data
Adam Walder, Ephraim M. Hanks, Aleksandra Slavković
In this work we develop methods for privatizing spatial location data, such as spatial locations of individual disease cases. We propose two novel Bayesian methods for generating s…
Causal inference in transportation safety studies: Comparison of potential outcomes and causal diagrams
Vishesh Karwa, Aleksandra B. Slavković, Eric T. Donnell
The research questions that motivate transportation safety studies are causal in nature. Safety researchers typically use observational data to answer such questions, but often wit…