Workshops

Spring 2019

Data Privacy: From Foundations to Applications

Mar 4, 2019 to Mar 8, 2019

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

Monday, March 4th, 2019
9:00 am–9:20 am		Coffee and Check-In
9:20 am–9:30 am		Opening Remarks
9:30 am–10:30 am		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 am–11:00 am		Coffee Break
11:00 am–11:45 am		Consistency with External Knowledge: The TopDown Algorithm Dan Kifer (Pennsylvania State University)
11:45 am–12:30 pm		Smooth Sensitivity for k-Nearest Neighbor Christopher Clifton (Purdue University)
12:30 pm–2:00 pm		Lunch
2:00 pm–2:45 pm		Hybrid Legal-Technical Concepts of Privacy Alexandra Wood (Harvard University)
2:45 pm–3:30 pm		Quantifying Privacy in Law Felix Wu (Cardozo School of Law)
3:30 pm–4:00 pm		Coffee Break
4:00 pm–4:45 pm		Discovery, Replication and Reuse of Sensitive Scientific Data with PSI James Honaker (Harvard University)
4:45 pm–5:30 pm		Modular Approach to Cloud Security Mayank Varia (Boston University)
5:30 pm–6:30 pm		Welcome Reception

Tuesday, March 5th, 2019
8:30 am–9:00 am		Coffee and Check-In
9:00 am–9:45 am		Working Towards Practical Machine Learning with Privacy Úlfar Erlingsson (Google Brain)
9:45 am–10:30 am		Protection Against Reconstruction and Its Applications in Private Federated Learning Ryan Rogers (Apple)
10:30 am–11:00 am		Coffee Break
11:00 am–11:45 am		Learning the Privacy-Utility Trade-off with Bayesian Optimization Borja Balle (Amazon)
11:45 am–12:30 pm		Differentially Private Trajectories Divesh Srivastava (AT&T)
12:30 pm–2:00 pm		Lunch
2:00 pm–2:45 pm		Formal Modelling of Information-flow Control Systems: Some Lessons Learned Dave Sands (Chalmers University of Technology)
2:45 pm–3:30 pm		Proving Differential Privacy via Relational Types Danfeng Zhang (Pennsylvania State University)
3:30 pm–4:15 pm		Automated Verification meets Differential Privacy Aws Albarghouthi (University of Wisconsin, Madison)
4:15 pm–4:45 pm		Coffee Break
4:45 pm–5:45 pm		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)

Wednesday, March 6th, 2019
8:30 am–9:00 am		Coffee and Check-In
9:00 am–9:45 am		A Practical Method to Reduce Privacy Loss when Disclosing Statistics Based on Small Samples John Friedman (Brown University)
9:45 am–10:30 am		From Health Data to Location Traces: Addressing Spatiotemporal Correlations in Differential Privacy Li Xiong (Emory University)
10:30 am–11:00 am		Coffee Break
11:00 am–11:45 am		Challenges in Scaling Privacy Enhancing Technologies to Campus Level IoT Testbed Sharad Mehrotra (UC Irvine)
11:45 am–12:30 pm		Practical Experience with Making Synthetic Data Differentially Private Gillian Raab (University of Edinburgh & Administrative Data Research Centre Scotland)
12:30 pm–2:00 pm		Lunch
2:00 pm–2:45 pm		Predicate Singling Out: Towards Formalizing the GDPR Notion of Singling Out Aloni Cohen (Massachusetts Institute of Technology)
2:45 pm–3:30 pm		What All We've Been Up To at Knexus Research Christine Task (Knexus Research Corp)
3:30 pm–4:15 pm		Accuracy Disparities and Social Choices in the Design of Privacy Mechanisms Gerome Miklau (University of Massachusetts Amherst)
4:15 pm–4:30 pm		Coffee Break

Thursday, March 7th, 2019
8:30 am–9:00 am		Coffee and Check-In
9:00 am–9:45 am		Differential Privacy from a Statistical Perspective – Obtaining Valid Inferences from Differentially Private Microdata Jörg Drechsler (Institute for Employment Research)
9:45 am–10:30 am		pMSE Mechanism: Differentially Private Synthetic Data with Maximal Distributional Similarity Joshua Snoke (RAND Corporation)
10:30 am–11:00 am		Coffee Break
11:00 am–11:45 am		Deploying DP: Experiences from the Front Lines and Open Challenges Ashwin Machanavajjhala (Duke University)
11:45 am–12:30 pm		Statistical Disclosure Limitation: Where do we go from here? Natalie Shlomo (University of Manchester)
12:30 pm–2:00 pm		Lunch
2:00 pm–2:45 pm		Matching Uses and Data Protections for Government Data Micah Altman (Massachusetts Institute of Technology)
2:45 pm–3:30 pm		Some Applications of Contextual Integrity Helen Nissenbaum (Cornell Tech)
3:30 pm–4:15 pm		DARPA and Privacy Research Joshua Baron (DARPA)
4:15 pm–4:45 pm		Coffee Break
4:45 pm–5:45 pm		PANEL: Where Are Statistical Agencies Going? Jörg Drechsler (Institute for Employment Research), Frauke Kreuter (University of Maryland), Natalie Shlomo (University of Manchester)

Friday, March 8th, 2019
8:30 am–9:00 am		Coffee and Check-In
9:00 am–9:45 am		Making Privacy Technology Accessible: Benchmarks and Platforms Michael Hay (Colgate University)
9:45 am–10:30 am		Towards Privacy-Respectful Cyber-Physical Systems: Two Perspectives Ruoxi Jia (UC Berkeley)
10:30 am–11:00 am		Coffee Break
11:00 am–11:30 am		What We’re Doing in Australia – An API for Confidentialized Contingency Tables Arthur Street (CSIRO Data61)
11:30 am–12:00 pm		Relational Symbolic Execution for Differential Privacy Gian Pietro Farina (University of Buffalo)
12:00 pm–12:30 pm		Privately Learning High-Dimensional Distributions Gautam Kamath (Massachusetts Institute of Technology)
12:30 pm–2:00 pm		Lunch & Poster Session
2:30 pm–3:15 pm		Open DP: A Proposal for an Open-Source Suite of Differential Privacy Tools Salil Vadhan (Harvard University)
3:15 pm–4:00 pm		Reflections on Deploying Differential Privacy in Industry Aleksandra Korolova (University of Southern California)
4:00 pm–4:30 pm		Coffee Break & Poster Session