23 citations · 39 across the 6 of their papers we have counts for
6 papers
A Consensus Privacy Metrics Framework for Synthetic Data
Lisa Pilgram, Fida K. Dankar, Jorg Drechsler +12
Synthetic data generation is one approach for sharing individual-level data. However, to meet legislative requirements, it is necessary to demonstrate that the individuals' privacy…
The Complexities of Differential Privacy for Survey Data
Jörg Drechsler, James Bailie
The concept of differential privacy (DP) has gained substantial attention in recent years, most notably since the U.S. Census Bureau announced the adoption of the concept for its 2…
30 Years of Synthetic Data
Joerg Drechsler, Anna-Carolina Haensch
The idea to generate synthetic data as a tool for broadening access to sensitive microdata has been proposed for the first time three decades ago. While first applications of the i…
Imputation under Differential Privacy
Soumojit Das, Jorg Drechsler, Keith Merrill +1
The literature on differential privacy almost invariably assumes that the data to be analyzed are fully observed. In most practical applications this is an unrealistic assumption.…
Applying Data Synthesis for Longitudinal Business Data across Three Countries
M. Jahangir Alam, Benoit Dostie, Jörg Drechsler +1
Data on businesses collected by statistical agencies are challenging to protect. Many businesses have unique characteristics, and distributions of employment, sales, and profits ar…
Controlling Privacy Loss in Sampling Schemes: an Analysis of Stratified and Cluster Sampling
Mark Bun, Jörg Drechsler, Marco Gaboardi +2
Sampling schemes are fundamental tools in statistics, survey design, and algorithm design. A fundamental result in differential privacy is that a differentially private mechanism r…