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1
Introduction
2
Differential Privacy
3
Mechanisms
4
Relaxations
5
L1 Sensitivity
6
Geometry
7
Why I love it
8
Where are we today
9
Facebook
10
Census Bureau
11
Activity
12
Overly Accurate Estimates
13
US Census
14
Database Reconstruction Theorem
15
Statistics
16
Challenges
17
Differentially Private Synthetic Data
18
Blum Liggett Roth
19
Hardness Results
20
Future Directions
21
Steve Feinberg
22
Two General Approaches
23
The Feinberg Problem
24
Sample and Aggregate
25
The Problem
26
Questions
Description:
Explore the concept of differential privacy in this distinguished lecture by Cynthia Dwork from Harvard University's Radcliffe Institute for Advanced Study. Delve into the mechanisms, relaxations, and geometric aspects of differential privacy, understanding its importance and current applications. Examine case studies involving Facebook and the US Census Bureau, and investigate challenges such as overly accurate estimates and database reconstruction. Learn about differentially private synthetic data, hardness results, and future directions in the field. Engage with the Feinberg problem and sample-and-aggregate approaches, gaining insights into this crucial aspect of data privacy and its implications for statistical analysis and societal impact.

Differential Privacy and the People's Data

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