Accounting for Intruder Uncertainty Due to Sampling When Estimating Identification Disclosure Risks in Partially Synthetic Data. - Jörg Drechsler; Jerome P. Reiter (Privacy in Statistical Databases, 2008)

Comparing Fully and Partially Synthetic Datasets for Statistical Disclosure Control in the German IAB Establishment Panel. - Jörg Drechsler; Stefan Bender; Susanne Rässler (Trans. Data Priv., 2008)

Remote Data Access and the Risk of Disclosure from Linear Regression: An Empirical Study. - Philipp Bleninger; Jörg Drechsler; Gerd Ronning (Privacy in Statistical Databases, 2010)

Using Support Vector Machines for Generating Synthetic Datasets. - Jörg Drechsler (Privacy in Statistical Databases, 2010)

An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets. - Jörg Drechsler; Jerome P. Reiter (Comput. Stat. Data Anal., 2011)

Generating Useful Test Data for Complex Linked Employer-Employee Datasets. - Matthias Dorner; Jörg Drechsler; Peter Jacobebbinghaus (Privacy in Statistical Databases, 2012)

Synthetic Longitudinal Business Databases for International Comparisons. - Jörg Drechsler; Lars Vilhuber (Privacy in Statistical Databases, 2014)

Some Clarifications Regarding Fully Synthetic Data. - Jörg Drechsler (PSD, 2018)

Secure Matrix Computation: A Viable Alternative to Record Linkage? - Jörg Drechsler; Benjamin Klein (PSD, 2020)

Controlling Privacy Loss in Survey Sampling (Working Paper). - Mark Bun; Jörg Drechsler; Marco Gaboardi; Audra McMillan (CoRR, 2020)

The R Package hmi: A Convenient Tool for Hierarchical Multiple Imputation and Beyond. - Matthias Speidel; Jörg Drechsler; Shahab Jolani (J. Stat. Softw., 2020)

Differential Privacy for Government Agencies - Are We There Yet? - Jörg Drechsler (CoRR, 2021)

Accuracy Gains from Privacy Amplification Through Sampling for Differential Privacy. - Jingchen Hu; Jörg Drechsler; Hang J. Kim (CoRR, 2021)

Non-parametric Differentially Private Confidence Intervals for the Median. - Jörg Drechsler; Ira Globus-Harris; Audra McMillan; Jayshree Sarathy; Adam D. Smith 001 (CoRR, 2021)

Controlling Privacy Loss in Sampling Schemes: An Analysis of Stratified and Cluster Sampling. - Mark Bun; Jörg Drechsler; Marco Gaboardi; Audra McMillan; Jayshree Sarathy (FORC, 2022)

Challenges in Measuring Utility for Fully Synthetic Data. - Jörg Drechsler (PSD, 2022)

30 Years of Synthetic Data. - Jörg Drechsler; Anna-Carolina Haensch (CoRR, 2023)

An Evaluation of Synthetic Data Generators Implemented in the Python Library Synthcity. - Emma Fössing; Jörg Drechsler (PSD, 2024)

Generating Synthetic Data is Complicated: Know Your Data and Know Your Generator. - Jonathan Latner; Marcel Neunhoeffer; Jörg Drechsler (PSD, 2024)

Evaluating the Pseudo Likelihood Approach for Synthesizing Surveys Under Informative Sampling. - Anna Oganian; Jörg Drechsler; Mehtab Iqbal (PSD, 2024)

The Complexities of Differential Privacy for Survey Data. - Jörg Drechsler; James Bailie (CoRR, 2024)

Foreword for the Collection of Papers from the Workshop on the Analysis of Census Noisy Measurement Files and Differential Privacy. - Jörg Drechsler; Ruobin Gong; Weijie Su 001; Linjun Zhang (J. Priv. Confidentiality, 2024)

A Consensus Privacy Metrics Framework for Synthetic Data. - Lisa Pilgram; Fida K. Dankar; Jörg Drechsler; Mark Elliot; Josep Domingo-Ferrer; Paul Francis; Murat Kantarcioglu; Linglong Kong; Bradley A. Malin; Krishnamurty Muralidhar; Puja Myles; Fabian Prasser; et al. (CoRR, 2025)