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
Presented under the auspices of the DIMACS Big Data Initiative on Privacy and Security and in collaboration with the Northeast Big Data Innovation Hub.

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

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

10:30 - 11:00  Break

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

12:00 -  1:30  Lunch

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

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

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

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

 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

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

 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	

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

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

10:30 - 11:00  Break		

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

11:30 - 12:00  Structure and Sensitivity in Differential privacy: Optimal K-norm mechanisms
               Aleksandra Slavkovic, Penn State University	

12:00 - 12:30  Differential Privacy for Relational Data -- A case study on Census Data
               Ashwin Machanavajjhala, Duke University

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	

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

 3:30 -  4:00  Break		

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

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

 5:00 PM       Conclude


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Document last modified on October 23, 2017.