Private Posterior Inference Consistent with Public Information: A Case Study in Small Area Estimation from Synthetic Census Data. - Jeremy Seeman; Aleksandra B. Slavkovic; Matthew Reimherr (PSD, 2020)

Exact Privacy Guarantees for Markov Chain Implementations of the Exponential Mechanism with Artificial Atoms. - Jeremy Seeman; Matthew Reimherr; Aleksandra B. Slavkovic (NeurIPS, 2021)

A Formal Privacy Framework for Partially Private Data. - Jeremy Seeman; Aleksandra B. Slavkovic; Matthew Reimherr (CoRR, 2022)

Statistical Data Privacy: A Song of Privacy and Utility. - Aleksandra B. Slavkovic; Jeremy Seeman (CoRR, 2022)

Differentially Private Finite Population Estimation via Survey Weight Regularization. - Jeremy Seeman; Yajuan Si; Jerome P. Reiter (CoRR, 2024)

Perspective: Better Privacy Theorists for Better Data Stewards. - Jeremy Seeman (J. Priv. Confidentiality, 2024)

Privately Answering Queries on Skewed Data via Per-Record Differential Privacy. - Jeremy Seeman; William Sexton; David Pujol; Ashwin Machanavajjhala (Proc. VLDB Endow., 2024)