Understanding Privacy and Quality Tradeoffs in Synthetic Network Data#

Authors: Andrew Chu (University of Chicago), Kyle MacMillan (University of Chicago), Paul Schmitt (Cal Poly), Nick Feamster (University of Chicago)

• Why: The closest thing to your home turf in the whole program: network measurement data (topologies, flow records, telemetry) is notoriously hard to share across orgs — vendors, operators, researchers — because it's simultaneously sensitive and operationally precious. Feamster's group doing a privacy/utility tradeoff study means real network datasets and network-quality metrics, not generic tabular-DP benchmarks.
• What: Apparently an empirical study of how privacy mechanisms applied when generating synthetic network data degrade the properties that make the data useful — quantifying the privacy/quality frontier (title/author line only — no abstract in program).
• How: Presumably generative models over network data (topology and/or traffic) with differential privacy or sanitization on the training side, evaluated on downstream network-analysis tasks.
• Where applicable: Broadly applicable to any org that shares network telemetry externally (benchmarks, incident collaboration, regulatory reporting, research datasets); not tied to a single operator's setup, though dataset specifics will matter.