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USBE outlines synthetic‑data pilot to let researchers test code without exposing student records
Summary
USBE presenters described a synthetic data effort using machine‑learning models and differential‑privacy checks to generate data that resemble LEA submissions; the goal is to enable external research and testing while protecting student privacy, with University of Utah code review underway.
A USBE presenter described an internal synthetic data project that produces artificial datasets resembling real LEA submissions so researchers and vendors can develop and test code without access to identifiable student records.
The presenter said the workflow trains machine‑learning models on real data to learn structure and then generates synthetic datasets that mimic distributions without containing real student entries. “No real student data…
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