Winning the NIST Contest: A scalable and general approach to differentially private synthetic data
We propose a general approach for differentially private synthetic data generation, that consists of three steps: (1) select a collection of low-dimensional marginals, (2) measure those marginals with a noise addition mechanism, and (3) generate synthetic data that preserves the measured marginals w...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Labor Dynamics Institute
2021-12-01
|
| Series: | The Journal of Privacy and Confidentiality |
| Subjects: | |
| Online Access: | http://journalprivacyconfidentiality.org/index.php/jpc/article/view/778 |