Marginal-based Methods for Differentially Private Synthetic Data - Differential Privacy for ML Series

Marginal-based Methods for Differentially Private Synthetic Data - Differential Privacy for ML Series

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Intro

1 of 17

1 of 17

Intro

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Marginal-based Methods for Differentially Private Synthetic Data - Differential Privacy for ML Series

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

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