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2020 – today
- 2024
[c12]Jonathan Latner, Marcel Neunhoeffer
, Jörg Drechsler:
Generating Synthetic Data is Complicated: Know Your Data and Know Your Generator. PSD 2024: 115-128- [c11]Anna Oganian, Jörg Drechsler, Mehtab Iqbal:
Evaluating the Pseudo Likelihood Approach for Synthesizing Surveys Under Informative Sampling. PSD 2024: 129-143 - [c10]Emma Fössing, Jörg Drechsler:
An Evaluation of Synthetic Data Generators Implemented in the Python Library Synthcity. PSD 2024: 178-193 - [i6]Jörg Drechsler, James Bailie:
The Complexities of Differential Privacy for Survey Data. CoRR abs/2408.07006 (2024) - 2023
- [i5]Jörg Drechsler, Anna-Carolina Haensch:
30 Years of Synthetic Data. CoRR abs/2304.02107 (2023) - 2022
- [c9]Mark Bun, Jörg Drechsler, Marco Gaboardi, Audra McMillan, Jayshree Sarathy:
Controlling Privacy Loss in Sampling Schemes: An Analysis of Stratified and Cluster Sampling. FORC 2022: 1:1-1:24 - [c8]Jörg Drechsler:
Challenges in Measuring Utility for Fully Synthetic Data. PSD 2022: 220-233 - 2021
- [i4]Jörg Drechsler:
Differential Privacy for Government Agencies - Are We There Yet? CoRR abs/2102.08847 (2021) - [i3]Jingchen Hu, Jörg Drechsler, Hang J. Kim:
Accuracy Gains from Privacy Amplification Through Sampling for Differential Privacy. CoRR abs/2103.09705 (2021) - [i2]Jörg Drechsler, Ira Globus-Harris, Audra McMillan, Jayshree Sarathy, Adam D. Smith:
Non-parametric Differentially Private Confidence Intervals for the Median. CoRR abs/2106.10333 (2021) - 2020
- [j3]Matthias Speidel, Jörg Drechsler, Shahab Jolani:
The R Package hmi: A Convenient Tool for Hierarchical Multiple Imputation and Beyond. J. Stat. Softw. 95(1) (2020) - [c7]Jörg Drechsler, Benjamin Klein:
Secure Matrix Computation: A Viable Alternative to Record Linkage? PSD 2020: 240-254 - [i1]Mark Bun, Jörg Drechsler, Marco Gaboardi, Audra McMillan:
Controlling Privacy Loss in Survey Sampling (Working Paper). CoRR abs/2007.12674 (2020)
2010 – 2019
- 2018
- [c6]Jörg Drechsler:
Some Clarifications Regarding Fully Synthetic Data. PSD 2018: 109-121 - 2014
- [c5]Jörg Drechsler, Lars Vilhuber:
Synthetic Longitudinal Business Databases for International Comparisons. Privacy in Statistical Databases 2014: 243-252 - 2012
- [c4]Matthias Dorner, Jörg Drechsler, Peter Jacobebbinghaus:
Generating Useful Test Data for Complex Linked Employer-Employee Datasets. Privacy in Statistical Databases 2012: 165-178 - 2011
- [j2]Jörg Drechsler, Jerome P. Reiter:
An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets. Comput. Stat. Data Anal. 55(12): 3232-3243 (2011) - 2010
- [c3]Jörg Drechsler:
Using Support Vector Machines for Generating Synthetic Datasets. Privacy in Statistical Databases 2010: 148-161 - [c2]Philipp Bleninger, Jörg Drechsler, Gerd Ronning:
Remote Data Access and the Risk of Disclosure from Linear Regression: An Empirical Study. Privacy in Statistical Databases 2010: 220-233
2000 – 2009
- 2008
- [j1]Jörg Drechsler, Stefan Bender, Susanne Rässler:
Comparing Fully and Partially Synthetic Datasets for Statistical Disclosure Control in the German IAB Establishment Panel. Trans. Data Priv. 1(3): 105-130 (2008) - [c1]Jörg Drechsler, Jerome P. Reiter:
Accounting for Intruder Uncertainty Due to Sampling When Estimating Identification Disclosure Risks in Partially Synthetic Data. Privacy in Statistical Databases 2008: 227-238
Coauthor Index
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