« DIMACS/Northeast Big Data Hub Workshop on Overcoming Barriers to Data Sharing including Privacy and Fairness
October 23, 2017 - October 24, 2017
Location:
DIMACS Center
Rutgers University
CoRE Building
96 Frelinghuysen Road
Piscataway, NJ 08854
Click here for map.
Organizer(s):
John Abowd, Cornell University
René Bastón, Columbia University
Tal Rabin, IBM
Adam Smith, Boston University
Salil Vadhan, Harvard University
Rebecca Wright, DIMACS
This workshop will bring together computer scientists, legal scholars, social scientists, and consumers of data to understand the extent to which privacy currently limits the sharing of data. Discussions will include but not be limited to research data and will seek to develop standards and best practices that enable new information flows in domains ranging from healthcare to energy. The workshop will also address issues of fairness of data-driven systems and processes, and their connections to privacy. These topics present central challenges for the flow of data within and between government, commercial, and academic institutions data that are crucial to address science, engineering, and policy challenges.
The workshop will address five major themes:
Related to each theme, there will be at least one tutorial aimed at entire workshop audience. These tutorials will summarize the state of the artin both research and practicerelated to that theme. There will also be one or more sessions on recent research related to each theme. The presentations in these sessions will explain the main ideas in a language accessible to a broad audience, but will include discussion aimed at specialists.
Monday, October 23, 2017
Welcome
How to Query Encrypted Data with Security against Persistent and Snapshot Adversaries
Seny Kamara, Brown University
Secure Multiparty Computation at Google
Ben Kreuter, Google
Secure Multiparty Computation for Scientific Research
Brett Hemenway, University of Pennsylvania
Break
Block chains: Tutorial and Lessons from Implementation
Elaine Shi, Cornell University
Lunch
Jana: Secure Computation with Differential Privacy, and Applications
Rebecca Wright, DIMACS
Towards Practical Differential Privacy for SQL Queries
Joseph Near, University of California, Berkeley
Differential Privacy in the Scientific Data Repository
James Honaker, Harvard University
Parallel Composition Revisited
Chris Clifton, Purdue University
Break
Bridging Privacy Definitions: Differential Privacy and Privacy Concepts from Law and Policy
Alexandra Wood, Harvard University
Releasing a Differentially Private Password Frequency Corpus from 70 Million Yahoo! Passwords
Jeremiah Blocki, Purdue University
Dinner at Panico's Restaurant
Tuesday, October 24, 2017
Development of Usable, Scalable MPC
Mayank Varia, Boston University
Guarding user Privacy with Federated Learning and Differential Privacy
Brendan McMahan, Google
Private Collection of Aggregate Statistics at Scale
Henry Corrigan-Gibbs, Stanford University
Break
Ilya Mironov, Google
Structure and Sensitivity in Differential Privacy: Optimal K-norm Mechanisms
Aleksandra Slavkovic, Pennsylvania State University
Lunch
2020 Decennial Census: Formal Privacy Implementation Update
Stephen Clark, United States Census Bureau
Philip Leclerc, United States Census Bureau
William Sexton, United States Census Bureau
Formal Verification of Differentially Private Mechanisms
Marco Gaboardi, University at Buffalo
Break
Explorations into Algorithmic Fairness
Rafael Pass, Cornell University
Privacy-Preserving Analytics for Correlated Data
Prateek Mittal, Princeton University
Participation is open to all, subject to space limitations. Please register if you'd like to attend.
Talks are mostly by invitation, but we will also consider submissions for potential talks. Please send the organizers an e-mail (to rebecca.wright at dimacs.rutgers.edu) if you'd like to be invited to speak with a description (no more than one page) of your planned topic, before October 6, 2017.
Sponsored by Northeast Big Data Innovation Hub and DIMACS, in association with the Big Data Initiative on Privacy and Security.