Welcome to PDA Privacy-preserving Distributed Algorithms A Solution for Next Generation Data Sharing

About Us

We have developed of a general framework of distributed learning (also known as collaborative learning) methods with novel inferential procedures that integrate sensitive biomedical data (e.g., EHR data and biobank data) across multiple institutes. We refer to this framework as PDA:Privacy-preserving Distributed Algorithms. Our algorithms are communication-efficient, which only require the collaborating sites to send aggregated information to the coordinating site once.

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Our Goal

Secure

Accurate

Efficient

Heterogeneity-Aware

Our Approaches

Our Partners

Penn Medicine

UnitedHealth Group

OHDSI

pcornet

One Florida – Clinical Research Consordium

Janssen

Children’s Hospital of Philadelphia

PEDSnet

The Stanford Medicine Research Data Repository (STARR)

Health Insurance Review & Assessment (HIRA)

IBM MarketScan®

Optum

UTHealth

SIDIAP

Columbia