Workshops

Spring 2019

Privacy and the Science of Data Analysis

Apr 8, 2019 to Apr 12, 2019

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

Monday, April 8th, 2019
9:00 am – 9:30 am		Coffee and Check-In
9:30 am – 9:45 am		Opening Remarks
9:45 am – 10:30 am		Locally Private Bayesian Inference for Count Models Steven Wu (University of Minnesota Twin Cities)
10:30 am – 11:15 am		Finite Sample Differentially Private Confidence Intervals Vishesh Karwa (Temple University)
11:15 am – 11:30 am		Break
11:30 am – 12:15 pm		Differential Privacy with Elliptical Stochastic Processes Matthew Reimherr (Pennsylvania State University)
12:15 pm – 2:00 pm		Lunch
2:00 pm – 2:45 pm		Robust Statistics and Bias Correction: Ideas for Differential Privacy and Utility Roberto Molinari (Pennsylvania State University)
2:45 pm – 3:15 pm		Break
3:15 pm – 4:00 pm		Differentially Private Inference for Binomial Data Jordan Awan (Pennsylvania State University)
4:00 pm – 4:45 pm		The Structure of Optimal Private Tests for Simple Hypotheses Audra McMillan (Boston University)
4:45 pm – 6:00 pm		Reception

Tuesday, April 9th, 2019
8:30 am – 9:00 am		Coffee and Check-In
9:00 am – 10:00 am		Practical Difficulties in the Application of Differential Privacy for the Release of Large-Scale Public Data Products Philip LeClerc (U.S. Census Bureau)
10:00 am – 10:45 am		Shrinkwrap: Differentially-Private Query Processing in Private Data Federations Johes Bater (Northwestern University)
10:45 am – 11:15 am		Break
11:15 am – 12:00 pm		Analyzing Administrative Data with Privacy Protection in Place Frauke Kreuter (University of Maryland)
12:00 pm – 12:45 pm		Differentially Private Inference for Regression Coefficients Andrés Felipe Barrientos (Duke University)
12:45 pm – 2:15 pm		Lunch
2:15 pm – 3:00 pm		Node Privacy, High-dimensional Graph Summaries, and Block Models Adam Smith (Boston University)
3:00 pm – 3:45 pm		Optimality in Locally Private Estimation and Learning John Duchi (Stanford University)
3:45 pm – 4:15 pm		Break
4:15 pm – 5:30 pm		PANEL: Statistical Theory, Privacy and Data Analysis Moderator: Aleksandra Slavkovic (Pennsylvania State University) \|\|\| Panelists: Anand Sarwate (Rutgers University), Thomas Steinke (IBM Almaden), Bin Yu (UC Berkeley), and Yu-Xiang Wang (UC Santa Barbara)

Wednesday, April 10th, 2019
8:30 am – 9:00 am		Coffee and Check-In
9:00 am – 9:45 am		Differentially Private Change-Point Detection Rachel Cummings (Georgia Institute of Technology)
9:45 am – 10:30 am		Bandit Learning under Differential Privacy Or Sheffet (University of Alberta)
10:30 am – 11:00 am		Break
11:00 am – 11:45 am		Pufferfish Privacy Mechanisms for Correlated Data Shuang Song (Google)
11:45 am – 1:15 pm		Lunch
1:15 pm – 2:00 pm		Black-box Random Differential Privacy with Sensitivity Sampling Ben Rubinstein (Melbourne University)
2:00 pm – 2:45 pm		The Privacy Blanket of the Shuffle Model Borja Balle (Amazon)
2:45 pm – 3:15 pm		Break
3:15 pm – 4:30 pm		Open Problem Session Moderator: Mark Bun (Princeton University)

Thursday, April 11th, 2019
8:30 am – 9:00 am		Coffee and Check-In
9:00 am – 9:45 am		Locally Private Statistical Inference: Communication, Samples, and Shared Randomness Jayadev Acharya (Cornell University)
9:45 am – 10:30 am		Privacy Amplification by Sampling and Renyi Differential Privacy Yu-Xiang Wang (UC Santa Barbara)
10:30 am – 11:00 am		Break
11:00 am – 11:45 am		Private Meta-Selection Kunal Talwar (Google)
11:45 am – 12:15 pm		Learning without Interaction Requires Separation Vitaly Feldman (Google)
12:15 pm – 1:45 pm		Lunch
1:45 pm – 2:30 pm		Private Boosting and Noise-tolerant Learning Mark Bun (Princeton University)
2:30 pm – 3:15 pm		Private Center Points and Learning of Halfspaces Amos Beimel (Ben Gurion University)
3:15 pm – 3:45 pm		Break
3:45 pm – 4:30 pm		Federated Heavy Hitters Discovery with Differential Privacy Peter Kairouz (Google AI)
4:30 pm – 5:15 pm		Private Learning with Auxiliary Public Data Raef Bassily (The Ohio State University)

Friday, April 12th, 2019
8:30 am – 9:00 am		Coffee and Check-In
9:00 am – 9:45 am		Is Interaction Necessary for Distributed Private Learning? Abhradeep Thakurta (UC Santa Cruz)
9:45 am – 10:30 am		Groups and Individuals in Fairness and Probability Cynthia Dwork (Harvard University)
10:30 am – 11:00 am		Break
11:00 am – 11:45 am		Towards a Characterization of Differentially Private Learning Shay Moran (Princeton University)
11:45 am – 1:30 pm		Lunch
1:30 pm – 2:15 pm		Smooth Sensitivity, Revisited Thomas Steinke (IBM Almaden)
2:15 pm – 3:00 pm		On the Power of Interactivity in Local Differential Privacy Aaron Roth (University of Pennsylania)
3:00 pm – 3:30 pm		Break
3:30 pm – 4:15 pm		The Limits of Post-Selection Generalization Uri Stemmer (Ben Gurion University)
4:15 pm – 5:00 pm		A Necessary and Sufficient Stability Notion for Adaptive Generalization Moshe Shenfeld (Hebrew University)