DIMACS/Northeast Big Data Hub Workshop on Overcoming Barriers to Data Sharing including Privacy and Fairness

October 23 - 24, 2017
DIMACS Center, CoRE Building, Rutgers University

Organizing Committee:

John Abowd, U.S. Census Bureau and Cornell University

René Bastón, Columbia University

Tal Rabin, IBM

Adam Smith, Boston University

Salil Vadhan, Harvard University

Rebecca Wright, Rutgers University

Workshop email: Barriers_workshop at email.rutgers.edu

Workshop Program:

Monday, October 23, 2017

 8:00 -  8:45  Breakfast and Registration		

 8:45 -  9:00  Welcome 		

 9:00 -	 9:30  How to Query Encrypted Data with Security against Persistent and Snapshot Adversaries
               Seny Kamara, Brown University

 9:30 - 10:00  Secure Multiparty Computation at Google
               Ben Kreuter, Google
               Video

10:00 -	10:30  Secure Multiparty Computation for Scientific Research  
               Brett Hemenway, University of Pennsylvania
               Video

10:30 - 11:00  Break

11:00 - 12:00  Block chains: Tutorial and lessons from implementation
               Elaine Shi, Cornell University
               Video

12:00 -  1:30  Lunch

 1:30 -  2:00  Jana: Secure Computation with Differential Privacy, and Applications
               Rebecca Wright, Rutgers University
               Video

 2:00 -  2:30  Towards Practical Differential Privacy for SQL Queries
	       Joseph Near, UC Berkeley
	       Video

 2:30 -  3:00  Differential Privacy in the Scientific Data Repository
               James Honaker, Harvard University
               Video

 3:00 -  3:30  Parallel Composition Revisited
               Chris Clifton, Purdue University
	       Video

 3:30 -  4:00  Break	

 4:00 -  5:00  Bridging Privacy Definitions: Differential Privacy and Privacy Concepts from Law and Policy					
               Alexandra Wood, Harvard University
               Video

 5:00 -  5:30  Releasing a Differentially Private Password Frequency Corpus
               Jeremiah Blocki, Purdue University
               Video

 7:00 PM       Dinner:  Panicos
               103 Church St.
               New Brunswick, NJ
               (P)732-545-6100
				
Tuesday, October 24, 2017

 8:00 -  9:00  Breakfast and Registration		

 9:00 -  9:30  Development of Usable, Scalable MPC
               Mayank Varia, Boston University
               Video

 9:30 - 10:00  Guarding user Privacy with Federated Learning and Differential Privacy
               Brendan McMahan, Google
               Video

10:00 - 10:30  Private Collection of Aggregate Statistics at Scale
               Henry Corrigan-Gibbs, Stanford University
               Video

10:30 - 11:00  Break		

11:00 - 11:30  Rényi Differential Privacy
               Ilya Mironov, Google
               Video

11:30 - 12:00  Structure and Sensitivity in Differential privacy: Optimal K-norm mechanisms
               Aleksandra Slavkovic, Penn State University	
	       Video
	       
12:00 - 12:30  Differential Privacy for Relational Data -- A case study on Census Data
               Ashwin Machanavajjhala, Duke University
	       Video

12:30 -  2:00  Lunch		

 2:00 -  3:00  2020 Decennial Census: Formal Privacy Implementation Update
               Philip Leclerc, Stephen Clark, William Sexton, US Census Bureau	
               Video

 3:00 -  3:30  Formal Verification of Differentially Private Mechanisms
               Marco Gaboardi, University at Buffalo
               Video

 3:30 -  4:00  Break		

 4:00 -  4:30  Explorations into Algorithmic Fairness
               Rafael Pass, Cornell University
               Video

 4:30 -  5:00  Privacy-Preserving Analytics for Correlated Data
               Prateek Mittal, Princeton University
	       Video

 5:00 PM       Conclude

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