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Classroom Contents
Marginal-based Methods for Differentially Private Synthetic Data - Differential Privacy for ML Series
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- 1 Intro
- 2 NIST DP Synthetic Data Competition
- 3 Competition Setup
- 4 Marginal-based mechanisms
- 5 Why Marginals?
- 6 Independent Baseline
- 7 MST Selection Algorithm
- 8 Bayesian Network vs. Markov Random Field
- 9 Select the Workload?
- 10 Interesting Empirical Finding
- 11 Considerations for Selection
- 12 Budget-Aware Mechanism
- 13 Workload-Aware Mechanism
- 14 Data-Aware Mechanism
- 15 Efficiency-Aware Mechanism
- 16 Qualitative Comparison of Prior Work
- 17 Summary & Open Problems
