Mathematical Foundations of Data Privacy
Videos from BIRS Workshop
Raef Bassily, Ohio State University
Monday Apr 30, 2018 09:14 - 09:48
Model-Agnostic Private Learning
Uri Stemmer, Harvard University
Monday Apr 30, 2018 10:47 - 11:25
Private k-Means with Constant Multiplicative Error
Thomas Steinke, Google
Monday Apr 30, 2018 14:26 - 15:16
Concentrated Differential Privacy
Omer Reingold, Stanford University
Monday Apr 30, 2018 15:51 - 16:58
On Algorithmic Fairness Between Groups and Individuals
Aleksandra Korolova, University of Southern California
Tuesday May 1, 2018 09:06 - 09:45
A Hybrid of Advocacy and Modeling for Differential Privacy
Matthew Reimherr, Pennsylvania State University
Tuesday May 1, 2018 09:47 - 10:26
Differential Privacy for Functional Data Analysis
Salil Vadhan, Harvard University
Tuesday May 1, 2018 10:50 - 11:53
PSI: A (differentially) private data-sharing interface
Peter Kairouz, Stanford University
Tuesday May 1, 2018 13:35 - 14:15
Generative Adversarial Privacy
Bo Waggoner, University of Pennsylvania
Tuesday May 1, 2018 14:16 - 14:48
Local Differential Privacy for Evolving Data
Rachel Cummings, Columbia University
Tuesday May 1, 2018 14:51 - 15:31
Individual Sensitivity Preprocessing for Data Privacy
Ilya Mironov, Google
Tuesday May 1, 2018 15:52 - 16:31
End-to-End Analysis of PATE
Vitaly Feldman, Apple
Tuesday May 1, 2018 16:32 - 17:26
Privacy-preserving prediction
Om Thakkar, Boston University
Wednesday May 2, 2018 09:07 - 09:34
Revisiting Differentially Private Matrix Completion
Kunal Talwar,
Wednesday May 2, 2018 09:36 - 10:16
Privacy Amplification by Iteration
Adam Smith, Boston University
Wednesday May 2, 2018 10:55 - 12:04
Bayesian models for adaptive data analysis
Jordan Awan, Pennsylvania State University
Thursday May 3, 2018 09:30 - 10:02
Comparing K-Norm Mechanisms
Aleksandar Nikolov, University of Toronto
Thursday May 3, 2018 10:33 - 11:44
Geometric Lower Bounds and Algorithms for Differential Privacy
Jonathan Ullman, Northeastern University
Thursday May 3, 2018 13:36 - 14:16
Privately learning high-dimensional distributions
Gautam Kamath, University of Waterloo
Thursday May 3, 2018 14:55 - 15:30
Differentially Private Hypothesis Testing and Property Estimation
Seth Neel, University of Pennsylvania
Thursday May 3, 2018 15:53 - 16:16
Accuracy First: Selecting a DP Level for Accurate ERM
Steven Wu, Carnegie Mellon University
Thursday May 3, 2018 19:41 - 20:12
DP GAN
Seth Neel, University of Pennsylvania
Friday May 4, 2018 09:49 - 10:37
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
