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

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Coffee and Check-In
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Opening Remarks
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Locally Private Bayesian Inference for Count Models
Steven Wu (University of Minnesota Twin Cities)
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Finite Sample Differentially Private Confidence Intervals
Vishesh Karwa (Temple University)
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Break
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Differential Privacy with Elliptical Stochastic Processes
Matthew Reimherr (Pennsylvania State University)
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Lunch
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Robust Statistics and Bias Correction: Ideas for Differential Privacy and Utility
Carlo Molinari (Pennsylvania State University)
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Break
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Differentially Private Inference for Binomial Data
Jordan Awan (Pennsylvania State University)
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The Structure of Optimal Private Tests for Simple Hypotheses
Audra McMillan (Boston University)
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Reception
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Coffee and Check-In
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Practical Difficulties in the Application of Differential Privacy for the Release of Large-Scale Public Data Products
Philip LeClerc (U.S. Census Bureau)
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Shrinkwrap: Differentially-Private Query Processing in Private Data Federations
Johes Bater (Northwestern University)
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Break
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Analyzing Administrative Data with Privacy Protection in Place
Frauke Kreuter (University of Maryland)
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Differentially Private Inference for Regression Coefficients
Andrés Felipe Barrientos (Duke University)
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Lunch
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Node Privacy, High-dimensional Graph Summaries, and Block Models
Adam Smith (Boston University)
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Optimality in Locally Private Estimation and Learning
John Duchi (Stanford University)
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Break
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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
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Coffee and Check-In
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Differentially Private Change-Point Detection
Rachel Cummings (Georgia Institute of Technology)
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Bandit Learning under Differential Privacy
Or Sheffet (University of Alberta)
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Break
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Pufferfish Privacy Mechanisms for Correlated Data
Shuang Song (Google)
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Lunch
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Black-box Random Differential Privacy with Sensitivity Sampling
Ben Rubinstein (Melbourne University)
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The Privacy Blanket of the Shuffle Model
Borja Balle (Amazon)
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Break
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Open Problem Session
Mark Bun (Princeton University)
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Coffee and Check-In
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Locally Private Statistical Inference: Communication, Samples, and Shared Randomness
Jayadev Acharya (Cornell University)
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Privacy Amplification by Sampling and Renyi Differential Privacy
Yu-Xiang Wang (UC Santa Barbara)
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Break
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Private Meta-Selection
Kunal Talwar (Apple Inc)
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Learning without Interaction Requires Separation
Vitaly Feldman (Apple)
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Lunch
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Private Boosting and Noise-tolerant Learning
Mark Bun (Princeton University)
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Private Center Points and Learning of Halfspaces
Amos Beimel (Ben Gurion University)
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Break
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Federated Heavy Hitters Discovery with Differential Privacy
Peter Kairouz (Google AI)
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Private Learning with Auxiliary Public Data
Raef Bassily (Ohio State University)
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Coffee and Check-In
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Is Interaction Necessary for Distributed Private Learning?
Abhradeep Guha Thakurta (UC Santa Cruz)
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Groups and Individuals in Fairness and Probability
Cynthia Dwork (Harvard University)
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Break
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Towards a Characterization of Differentially Private Learning
Shay Moran (Technion - Israel Institute for Technology)
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Lunch
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Smooth Sensitivity, Revisited
Thomas Steinke (IBM Almaden)
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On the Power of Interactivity in Local Differential Privacy
Aaron Roth (University of Pennsylvania)
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Break
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The Limits of Post-Selection Generalization
Uri Stemmer  (Ben Gurion University)
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A Necessary and Sufficient Stability Notion for Adaptive Generalization
Moshe Shenfeld (Hebrew University)