Introduction and Setup
Introduction to Data Privacy and Data Synthesis Techniques
Preface
Introduction and Setup
1
Introduction to Data Privacy
Data Privacy and Synthesis
2
Synthetic Data
3
Utility and Disclosure Risk Metrics
4
Data Synthesis Demo
5
Synthetic Data Case Studies
Differential Privacy
6
Introduction to Differential Privacy and Formal Privacy
7
Formally Private Definitions, Fundamental Mechanisms, and Algorithms
8
Formally Private Case Studies
Where to Go From Here
References
Table of contents
Urban Institute
Aaron R. Williams
Roles
Projects
Questions for You
Course Structure
Introduction and Setup
Urban Institute
Non-partisan and not-for-profit social and economic policy research institution headquartered in Washington, DC
Aaron R. Williams
Roles
Lead Data Scientist for Statistical Computing at the
Urban Institute
Adjunct Professor in the McCourt School of Public Policy at Georgetown University
American Statistical Association Traveling Course Instructor
Projects
Synthetic data generation (
rstudio::conf(2022) talk about
library(tidysynthesis)
)
Formal privacy/differential privacy evaluation
A Feasibility Study of Differentially Private Summary Statistics and Regression Analyses with Evaluations on Administrative and Survey Data (
code
) (
JASA
)
Benchmarking DP Linear Regression Methods for Statistical Inference (
Preprint
)
Projects that iterate with R Markdown/Quarto
Mobility Metrics data pages
State Fiscal briefs
Manage the Urban Institute ggplot2 theme (
Examples
) (
Code
)
Urban Institute R Users Group
Questions for You
What types of analyses do you develop?
What is your programming experience?
What are you most interested to learn today?
Course Structure
Introduction to Data Privacy
Synthetic Data
Formal Privacy and Differential Privacy
Preface
1
Introduction to Data Privacy
