TRACK 1: Advancing key technologies to enable practical PPDSA solutions

PDaSP Track 1: Enabling a Privacy-Preserving Data Life Cycle with Lightweight Secure Computation, Henry Corrigan-Gibbs (PI) and Emma Dauterman (Co-PI), Massachusetts Institute of Technology
[NSF Award Abstract]

PDaSP Track 1: Practical Secure Multiparty Computations for Graph-based Intrusion Detection Systems, Yupeng Zhang (PI), University of Illinois at Urbana-Champaign and Li Zhou (Co-PI), University of California-Irvine
[NSF Award Abstract]

TRACK 2: Integrated and comprehensive solutions for trustworthy data sharing in application settings

PDaSP Track 2: A Holistic Privacy Preserving Collaborative Data Sharing System for Intelligent Transportation, Xuegang Ban (PI) and Angela Kitali (Co-PI), University of Washington, Yuan Hong (PI) and Song Han (Co-PI), University of Connecticut, Binghui Wang (PI), Illinois Institute of Technology and Meisam Mohammady (PI), Iowa State University
[NSF Award Abstract] [Project Website]

PDaSP Track 2: Confidential Genome Imputation and Analytics (CoGIA), Hyunghoon Cho (PI), Suleyman Sahinalp (Co-PI) and Fan Zhang (Co-PI), Yale University
[NSF Award Abstract]

PDaSP Track 2: Explainable Auditing of ML Models for Privacy Violations, Bradley Malin (PI), Vanderbilt University Medical Center, Netanel Raviv (PI) and Yevgeniy Vorobeychik (Co-PI), Washington University, and Murat Kantarcioglu (PI), Virginia Polytechnic Institute and State University
[NSF Award Abstract]

PDASP Track 2: TIDES - Building a Trusted Integration Data Exchange System, Sebastian Angel (PI), Andreas Haeberlen (Co-PI), Brett Falk (Co-PI), Ryan Marcus (Co-PI) and Pratyush Mishra (Co-PI), University of Pennsylvania
[NSF Award Abstract] [Project Website]

TRACK 3: Usable tools, and testbeds for trustworthy sharing of private or otherwise confidential data

PDaSP Track 3: Privacy-Preserving Dairy-Digitalization with Federated Learning, Miel Hostens (PI), and Joao Dorea (Co-PI), Cornell University
[NSF Award Abstract]

PDaSP Track 3: Testbed for Enhancing Privacy and Robustness of Federated Learning Systems, Fatima Anwar (PI), University of Massachusetts Amherst, Muhammad Ali Gulzar (PI), Virginia Polytechnic Institute and State University, and Ali Anwar (PI), University of Minnesota-Twin Cities
[NSF Award Abstract]

PDaSP: Track 3: Rigorous and Performant Differentially Private Machine Learning via OpenDP, Salil Vadhan (PI) and Flavio Calmon (Co-PI), Harvard University
[NSF Award Abstract]

PDaSP: Track 3: TEPPIT: TEstbed for Privacy-PreservIng Technologies for Data Sharing and Analysis, Jelena Mirkovic (PI), John Heidemann (Co-PI) and Jose-Luis Ambite (Co-PI), University of Southern California
[NSF Award Abstract]