• Click on the titles of individual talks for abstract, slides and archived video.

–
Coffee and Check-In
–
Opening Remarks
–
Locally Private Bayesian Inference for Count Models

Steven Wu (University of Minnesota Twin Cities)
Video
–
Finite Sample Differentially Private Confidence Intervals

Vishesh Karwa (Temple University)
Video
–
Break
–
Differential Privacy with Elliptical Stochastic Processes

Matthew Reimherr (Pennsylvania State University)
Video
–
Lunch
–
Robust Statistics and Bias Correction: Ideas for Differential Privacy and Utility

Carlo Molinari (Pennsylvania State University)
Video
–
Break
–
Differentially Private Inference for Binomial Data

Jordan Awan (Pennsylvania State University)
Video
–
The Structure of Optimal Private Tests for Simple Hypotheses

Audra McMillan (Boston University)
Video
–
Reception
–
Coffee and Check-In
–
Practical Difficulties in the Application of Differential Privacy for the Release of Large-Scale Public Data Products

Philip LeClerc (U.S. Census Bureau)
Video
–
Shrinkwrap: Differentially-Private Query Processing in Private Data Federations

Johes Bater (Northwestern University)
Video
–
Break
–
Analyzing Administrative Data with Privacy Protection in Place

Frauke Kreuter (University of Maryland)
Video
–
Differentially Private Inference for Regression Coefficients

Andrés Felipe Barrientos (Duke University)
Video
–
Lunch
–
Node Privacy, High-dimensional Graph Summaries, and Block Models

Adam Smith (Boston University)
Video
–
Optimality in Locally Private Estimation and Learning

John Duchi (Stanford University)
Video
–
Break
–
PANEL: Statistical Theory, Privacy and Data Analysis

Aleksandra Slavkovic (Pennsylvania State University),
Anand Sarwate (Rutgers University),
Thomas Steinke (IBM Almaden),
I,
Bin Yu (UC Berkeley),
Yu-Xiang Wang
Video
–
Coffee and Check-In
–
Differentially Private Change-Point Detection

Rachel Cummings (Georgia Institute of Technology)
Video
–
Bandit Learning under Differential Privacy

Or Sheffet (University of Alberta)
Video
–
Break
–
Pufferfish Privacy Mechanisms for Correlated Data

Shuang Song (Google)
Video
–
Lunch
–
Black-box Random Differential Privacy with Sensitivity Sampling

Ben Rubinstein (Melbourne University)
Video
–
The Privacy Blanket of the Shuffle Model

Borja Balle (Amazon)
Video
–
Break
–
Open Problem Session

Mark Bun (Princeton University)
Video
–
Coffee and Check-In
–
Locally Private Statistical Inference: Communication, Samples, and Shared Randomness

Jayadev Acharya (Cornell University)
Video
–
Privacy Amplification by Sampling and Renyi Differential Privacy

Yu-Xiang Wang (UC Santa Barbara)
Video
–
Break
–
Private Meta-Selection

Kunal Talwar (Apple Inc)
Video
–
Learning without Interaction Requires Separation

Vitaly Feldman (Apple)
Video
–
Lunch
–
Private Boosting and Noise-tolerant Learning

Mark Bun (Princeton University)
Video
–
Private Center Points and Learning of Halfspaces

Amos Beimel (Ben Gurion University)
Video
–
Break
–
Federated Heavy Hitters Discovery with Differential Privacy

Peter Kairouz (Google AI)
Video
–
Private Learning with Auxiliary Public Data

Raef Bassily (Ohio State University)
Video
–
Coffee and Check-In
–
Is Interaction Necessary for Distributed Private Learning?

Abhradeep Guha Thakurta (UC Santa Cruz)
Video
–
Groups and Individuals in Fairness and Probability

Cynthia Dwork (Harvard University)
Video
–
Break
–
Towards a Characterization of Differentially Private Learning

Shay Moran (Technion - Israel Institute for Technology)
Video
–
Lunch
–
Smooth Sensitivity, Revisited

Thomas Steinke (IBM Almaden)
Video
–
On the Power of Interactivity in Local Differential Privacy

Aaron Roth (University of Pennsylvania)
Video
–
Break
–
The Limits of Post-Selection Generalization

Uri Stemmer  (Ben Gurion University)
Video
–
A Necessary and Sufficient Stability Notion for Adaptive Generalization

Moshe Shenfeld (Hebrew University)
Video