This document discusses differential privacy and how it allows private data to be shared while protecting individuals' privacy. It introduces differential privacy as a concept where statistics produced from datasets are insensitive to changes in individuals' data. It then provides examples of how differentially private techniques can be used to share summary statistics like means, histograms, and linear regression results from private datasets while adding just enough mathematical noise to the results to prevent individuals from being reidentified.

1. Differential Privacy
Releasing Private Summary Statistics
Connor Bain & Victor Balcer
Or Sheffet, Vito D’Orazio, Kobbi Nissim, Mark Bun, & Salil Vadhan

3. Dataset #1 Dataset #2
Dataverse

4. Dataverse
Dataset
Dataset Dataset
Dataverse
Dataset Dataset
Dataverse
Dataset Dataset
Dataverse
Dataverse
Dataset

5. Sharing Data
Collect Research Data
Database
Public Access

6. Collect Research Data
Database
Public Access to
Summary Statistics
Mean: 20
Limited Access

8. Differential Privacy
A function (statistic) is -differentially
private if for all neighboring databases,

9. Differentially Private Access
Collect Research Data
Database
Perturbed
Summary
Statistics
Mean: 20 Mean: 22 +/- 3

10. Laplace Distribution

12. Means

14. Variance-Covariance Matrix

16. Linear Regression

17. Histograms
Categorical Data
- Unordered values

18. Histograms
Bins
Counts

19. Histograms
Bins
Counts

21. Questions?