Conference Schedule

Tentative schedule, subject to change.

Schedule

Day 1	Friday September 13, 2024
8:45am - 9:00am	Introduction
9:00am - 10:00am	Invited panel - “Best Practices for Sharing Data & Future Implications for Other Domains” Temilola Afolabi at CODE, Kelsey Finch at Snowflake, Gabe Kaptchuk at University of Maryland, and Nawar Shara at Georgetown University (panelists’ bios at the the end) The sharing of administrative and survey data are important for understanding various policy outcomes, but such increased data access for research and public policy requires the framework of various regulations that seek to ensure patient privacy. For example, various laws and regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) and National Institutes of Health guidelines for Protecting Participant Privacy when Sharing Scientific Data, strive to protect the privacy of the identities of individuals and their health-related data. As a possible solution within these regulatory requirements, the Harvard Data Commons, the Open Commons Consortium, the National Cancer Institute’s Genomic Data Commons, and other entities have developed public data access systems that curate and host health-related and other data for research and public policy decisions. This session will bring together experts in this arena to discuss key developments on balancing data privacy and access as well as the future of data access, with a goal of identifying the lessons learned that may across several domains and contexts.
10:00am - 10:15am	Coffee and tea break
10:15am - 12:00pm	Session 1: Government PETs Applications
	S1-01: Lisa B. Mirel, Cordell Golden, Rob Zybrick, Rui Wang, Chrystine Tadler, and Christine Cox - Linking Data in a Shared Service Environment
	S1-02: Silke Taylor, Josh Miller, Erika Tyagi, Deena Tamaroff, Clayton Seraphin, Graham MacDonald, Claire McKay Bowen, and Aaron R. Williams - Building an Automated Validation Server Prototype
	S1-03: Cordell Golden - National Center for Health Statistics Data Linkage Program: Generating Synthetic Data to Support Tiered Access
	S1-04: Shlomi Hod and Ran Canetti - Designing the Pilot Release of Israel’s National Registry of Live Births: Reconciling Privacy with Accuracy and Usability
12:00pm - 1:30pm	Lunch
1:30pm - 3:15pm	Session 2: DP Methods and Applications
	S2-01: Rob Chew, Terrance D. Savitsky, and Matthew R. Williams - Privacy Preserving Autocoders
	S2-02: Ogonnaya Romanus, Younes Boulaguiem, Roberto Molinari, and Stephane Guerrier - Simulation-Based Differentially Private Inference for Proportions
	S2-03: Liudas Panavas, Hari Bhimaraju, Wynee Pintado, Rebecca N. Wright, and Rachel Cummings - A Visualization Tool To Help Technical Practitioners of Differential Privacy
	S2-04: Priyanka Nanayakkara, Jayshree Sarathy, Mary Anne Smart, Rachel Cummings, Gabriel Kaptchuk, and Elissa M. Redmiles - Eliciting End Users’ Privacy-Accuracy Preferences for Differentially Private Deployments
3:15pm - 3:30pm	Coffee and tea break
3:30pm - 5:00pm	Working Roundtables - Practical Applications and Policy Decisions of PETs Roundtable leaders Saki Kinney, RTI (Synthetic Data) Michael Hawes, U.S. Census Bureau (Formal Privacy) Ashwin Machanavajjhala, Tumult Labs (Formal Privacy) Sarah Scheffler Carnegie Mellon University (Secure Multiparty Computing) Christine Task, Knexus Research Corporation (Synthetic Data)
5:15pm - 7:00pm	Poster session P1-01: Lu Chen, Luca Sartore, Michael Jacobsen, Kado Yeo, and Valbona Bejleri - Bayesian data synthesis for data with weights P1-02: Christian Cianfarani and Aloni Cohen - Differential Privacy Does Not Prevent Lawful Redistricting P1-03: Gary Howarth and Christine Task - Benchmarking privacy-preserving synthetic data with the collaborative research cycle project P1-04: Michael Jacobsen, Kado Yeo, Luca Sartore, and Chen Lu - Synthetic Microdata Methods For Confidential Agricultural Data P1-05: JN Matthews and Aloni Cohen - Whose 2010 Census responses can be reconstructed with certainty? P1-06: Vasundhara Kaul and Tamalika Mukherjee - Equitable Differential Privacy P1-07: Casey Meehan, David Pujol, Damien Desfontaines, and Ashwin Machanavajjhala - Is My Synthetic Data Actually Private? A Framework For Empirical Privacy Metrics P1-08: Sarah Radway, Katherine Quintanilla, Cordelia Ludden, and Daniel Votipka - An Investigation of US Universities’ Implementation of FERPA Student Directory Policies P1-09: Samuel Dooley, Dana Turjeman, John P Dickerson, and Elissa M. Redmiles - The Impact of Statements about Privacy & Data Collection On Privacy-Sensitive App Adoption P1-10: Jingchen Hu, Matthew R. Williams, and Terrance D. Savitsky - Mechanisms for Global Differential Privacy under Bayesian Data Synthesis P1-11: Jeremy Seeman, Palak Jain, and Daniel Susser - Privacy’s odd couple: privacy law and privacy engineering on inference and information recovery P1-12: Amy O’Hara and Stephanie Straus - An Exploration of Secure Enclaves to Increase State Agency Data Access P1-13: Ruizhe Wang, Aarushi Dubey, Roberta De Viti, Deepak Garg, and Elissa M. Redmiles - The Role of Privacy Guarantees in Voluntary Donation of Private Data for Altruistic Goals P1-14: Hang J. Kim, Terrance Savitsky, Matt Williams, and Monika Hu - Synthetic population generation for nested data using differentially private posteriors P1-15: Alexandra Wood and Marco Gaboardi - The Role of Synthetic Data in Privacy Governance P1-16: Amina Abdu, Jeremy Seeman, and Abigail Jacobs - Legitimacy strategies and privacy-enhancing technology policy

Day 2	Saturday September 14, 2024
9:00am - 10:45am	Session 3: Synthetic Data Methods and Applications
	S3-01: Luca Sartore, Lu Chen, Michael Jacobsen, Kado Yeo, and Valbona Bejleri - High-Dimensional Synthetic Data Via Nearest Neighbors
	S3-02: Medha Uppala, Minsun Riddles, Tom Krenzke, Natalie Shlomo, Angela Chen, Robyn Ferg, and Lin Li - Research and Applications of Select Synthetic Data Approaches at Westat
	S3-03: Ayat Almomani, Hang J. Kim, Won Chang, and Youngdeok Hwang - Automatic Variance Adjusted Bayesian Inference with Pseudo Likelihood under Unequal Probability Sampling: Imputation and Data Synthesis
	S3-04: Cameron Bale and Harrison Quick - Optimized Sequential Synthesis with an Application to Legal Anonymization
10:45am - 11:00am	Coffee and tea break
11:00am - 12:45pm	Session 4: Privacy Perspectives
	S4-01: Elan Segarra - Estimating Preferences Over Data to Inform Statistical Disclosure Methods
	S4-02: Severin Engelmann and Helen Nissenbaum - Countering Privacy Nihilism
	S4-03: James Bailie, Roubin Gong, and Xiao-Li Meng - Can Swapping be Differentially Private? A Refreshment Stirred, not Shaken
	S4-04: Rachel Cummings, Talia Gillis, and Tamalika Mukherjee - The Hidden Costs of Privacy Choice for Marginalized Groups
12:45pm - 2:00pm	Lunch and wrap-up

Panelist Information

Temilola Afolabi is the Program Manager for CODEs Data for Equity Program. In this role, Temi leads the organization’s Open Data for Equity program. In this role, she aims to support state and local leaders to identify equity-related needs and apply local data for effective policy solutions and technical resource creation. Over the past 4 years, the program has had success in applying data to improve equity in fair housing, criminal justice, environmental justice, healthcare, and workforce opportunity. Since 2019, Temi has supported CODEs wider research efforts as its Research and Communications Intern, Research Associate, and Senior Research Associate. Her work helped support the planning, implementation, and evaluation of social good projects, resulting in data-informed technical documents, policy recommendations, and actionable next steps. In 2023, Temi served as the In-Country Project Manager for a USAID conflict prevention project with the UMD Center for International Development and Conflict Management. Based in Tamale, Ghana, she was responsible for ensuring the successful implementation of project goals and working directly with communities to reduce the threat of extremist violence in Northern Ghana. Temi is currently pursuing her MSc in Data Analytics at the University of Maryland. She completed the National Institute of Health’s Data Science Fellowship with the Agency’s Artificial Intelligence and Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) program in 2022. She received her Bachelor’s degree from the University of Maryland College Park, where she studied International Government and Politics, with a Minor certificate in International Development and Conflict Management. She can be reached at temilola@odenterprise.org.

Kelsey is an Adjunct Professor with the William & Mary School of Law and Senior Corporate Counsel - Privacy at Snowflake. An experienced privacy attorney, Kelsey advises clients in building and maturing their privacy programs.

Previously, Kelsey was Senior Privacy Counsel at the Future of Privacy Forum where she led FPF’s work on privacy enhancing technologies and responsible data sharing, among other projects. Kelsey collaborated with senior privacy leaders in government, academia, industry, and civil society to build best practices, inform sound policymaking, and create educational materials to advance responsible data practices for emerging technologies.

She is a graduate of Smith College and the Benjamin N. Cardozo School of Law, with a concentration in Intellectual Property and Information Law. She is a Certified Information Privacy Professional (CIPP/US) and a Certified Information Privacy Manager (CIPM).

Dr. Gabe Kaptchuk is an Assistant Professor of Computer Science at the University of Maryland, College Park. His research is inspired by a desire to prepare cryptographic systems for high-impact, real-world deployment. Dr. Kaptchuk’s research spans multiple subdomains of security and privacy, including applied cryptography, theoretical cryptography, and human factors. Dr. Kaptchuk also works at the intersections of policy, law, and computer science and teaches interdisciplinary courses at these intersections.

Dr. Shara is the founding director of AI CoLab, chief research data science, director of the Center of Biostatistics, Informatics, Data Science at the MedStar Health Research Institute, and Associate Professor of Medicine at Georgetown university with over 20 years of experience. As an NIH-funded investigator, she leads multidisciplinary teams to harness the power of Artificial Intelligence in promoting ethical, transparent, and trustworthy solutions in healthcare. Dr. Shara has several leadership positions spanning the CTSA/NCATS consortium to the national AIM-AHEAD program, charged with raising awareness about the potential role AI plays in health equity. She leads the development of curricula focused on the role AI plays in healthcare to train and educate the next generation of frontline healthcare workers. Dr. Shara earned her Master and PhD in applied statistics from American University after completing her undergraduate degree in Economics at Damascus University.