Data Privacy: From Foundations to Applications

Program

Data Privacy: Foundations and Applications

Location

Calvin Lab auditorium

Date

Monday, Mar. 4 – Friday, Mar. 8, 2019

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

9 – 9:20 a.m.

Coffee and Check-In

9:20 – 9:30 a.m.

Opening Remarks

9:30 – 10:30 a.m.

Stepping-up: The Census Bureau Tries to Be a Good Data Steward in the 21st Century

John Abowd (U.S. Census Bureau; Cornell University)

10:30 – 11 a.m.

Coffee Break

11 – 11:45 a.m.

Consistency with External Knowledge: The TopDown Algorithm

Dan Kifer (Pennsylvania State University)

11:45 a.m. – 12:30 p.m.

Smooth Sensitivity for k-Nearest Neighbor

Christopher Clifton (Purdue University)

12:30 – 2 p.m.

Lunch

2 – 2:45 p.m.

Hybrid Legal-Technical Concepts of Privacy

Alexandra Wood (Harvard University)

2:45 – 3:30 p.m.

Quantifying Privacy in Law

Felix Wu (Cardozo School of Law)

3:30 – 4 p.m.

Coffee Break

4 – 4:45 p.m.

Discovery, Replication and Reuse of Sensitive Scientific Data with PSI

James Honaker (Harvard University)

4:45 – 5:30 p.m.

Modular Approach to Cloud Security

Mayank Varia (Boston University)

5:30 – 6:30 p.m.

Welcome Reception

8:30 – 9 a.m.

Coffee and Check-In

9 – 9:45 a.m.

Working Towards Practical Machine Learning with Privacy

Úlfar Erlingsson (Google Brain)

9:45 – 10:30 a.m.

Protection Against Reconstruction and Its Applications in Private Federated Learning

Ryan Rogers (Apple)

10:30 – 11 a.m.

Coffee Break

11 – 11:45 a.m.

Learning the Privacy-Utility Trade-off with Bayesian Optimization

Borja Balle (Amazon)

11:45 a.m. – 12:30 p.m.

Differentially Private Trajectories

Divesh Srivastava (AT&T)

12:30 – 2 p.m.

Lunch

2 – 2:45 p.m.

Formal Modelling of Information-flow Control Systems: Some Lessons Learned

Dave Sands (Chalmers University of Technology)

2:45 – 3:30 p.m.

Proving Differential Privacy via Relational Types

Danfeng Zhang (Pennsylvania State University)

3:30 – 4:15 p.m.

Automated Verification meets Differential Privacy

Aws Albarghouthi (University of Wisconsin, Madison)

4:15 – 4:45 p.m.

Coffee Break

4:45 – 5:45 p.m.

PANEL: Setting & Managing the Privacy Budget

John Abowd (U.S. Census Bureau; Cornell University), Úlfar Erlingsson (Google Brain), Aleksandra Korolova (University of Southern California), Ian Schmutte (University of Georgia), Alexandra Wood (Harvard University)

8:30 – 9 a.m.

Coffee and Check-In

9 – 9:45 a.m.

A Practical Method to Reduce Privacy Loss when Disclosing Statistics Based on Small Samples

John Friedman (Brown University)

9:45 – 10:30 a.m.

From Health Data to Location Traces: Addressing Spatiotemporal Correlations in Differential Privacy

Li Xiong (Emory University)

10:30 – 11 a.m.

Coffee Break

11 – 11:45 a.m.

Challenges in Scaling Privacy Enhancing Technologies to Campus Level IoT Testbed

Sharad Mehrotra (UC Irvine)

11:45 a.m. – 12:30 p.m.

Practical Experience with Making Synthetic Data Differentially Private

Gillian Raab (University of Edinburgh & Administrative Data Research Centre Sco…

12:30 – 2 p.m.

Lunch

2 – 2:45 p.m.

Predicate Singling Out: Towards Formalizing the GDPR Notion of Singling Out

Aloni Cohen (Massachusetts Institute of Technology)

2:45 – 3:30 p.m.

What All We've Been Up To at Knexus Research

Christine Task (Knexus Research Corp)

3:30 – 4:15 p.m.

Accuracy Disparities and Social Choices in the Design of Privacy Mechanisms

Gerome Miklau (University of Massachusetts Amherst)

4:15 – 4:30 p.m.

Coffee Break

8:30 – 9 a.m.

Coffee and Check-In

9 – 9:45 a.m.

Differential Privacy from a Statistical Perspective – Obtaining Valid Inferences from Differentially Private Microdata

Jörg Drechsler (Institute for Employment Research)

9:45 – 10:30 a.m.

pMSE Mechanism: Differentially Private Synthetic Data with Maximal Distributional Similarity

Joshua Snoke (RAND Corporation)

10:30 – 11 a.m.

Coffee Break

11 – 11:45 a.m.

Deploying DP: Experiences from the Front Lines and Open Challenges

Ashwin Machanavajjhala (Duke University)

11:45 a.m. – 12:30 p.m.

Statistical Disclosure Limitation: Where do we go from here?

Natalie Shlomo (University of Manchester)

12:30 – 2 p.m.

Lunch

2 – 2:45 p.m.

Matching Uses and Data Protections for Government Data

Micah Altman (Massachusetts Institute of Technology)

2:45 – 3:30 p.m.

Some Applications of Contextual Integrity

Helen Nissenbaum (Cornell Tech)

3:30 – 4:15 p.m.

DARPA and Privacy Research

Joshua Baron (DARPA)

4:15 – 4:45 p.m.

Coffee Break

4:45 – 5:45 p.m.

PANEL: Where Are Statistical Agencies Going?

Jörg Drechsler (Institute for Employment Research), Frauke Kreuter (University of Maryland), Natalie Shlomo (University of Manchester)

8:30 – 9 a.m.

Coffee and Check-In

9 – 9:45 a.m.

Making Privacy Technology Accessible: Benchmarks and Platforms

Michael Hay (Colgate University)

9:45 – 10:30 a.m.

Towards Privacy-Respectful Cyber-Physical Systems: Two Perspectives

Ruoxi Jia (UC Berkeley)

10:30 – 11 a.m.

Coffee Break

11 – 11:30 a.m.

What We’re Doing in Australia – An API for Confidentialized Contingency Tables

Arthur Street (CSIRO Data61)

11:30 a.m. – 12 p.m.

Relational Symbolic Execution for Differential Privacy

Gian Pietro Farina (University at Buffalo, SUNY)

12 – 12:30 p.m.

Privately Learning High-Dimensional Distributions

Gautam Kamath (Massachusetts Institute of Technology)

12:30 – 2 p.m.

Lunch & Poster Session

2:30 – 3:15 p.m.

Open DP: A Proposal for an Open-Source Suite of Differential Privacy Tools

Salil Vadhan (Harvard University)

3:15 – 4 p.m.

Reflections on Deploying Differential Privacy in Industry

Aleksandra Korolova (University of Southern California)

4 – 4:30 p.m.

Coffee Break & Poster Session