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OONI is guarding its data against bad measurements — what that means if you build on it

We build on OONI's public dataset. Our own work tracks how well Taiwan and the wider APAC region are actually observed in that data, and our Run v2 census mapped how the whole Run v2 ecosystem gets used. So when OONI published a long engineering post on [how it detects and mitigates faulty measurements]1 — alongside a new anonymous-credential system now rolling into production — we read it not as OONI insiders but as people downstream who use this data to make claims about a thinly-observed part of the world.

Here is what stands out from that seat.

OONI Run v2 usage census

Worldwide, OONI Run v2 has produced 14.17 million measurements, and just three lists account for 72% of them. The highest-volume lists all work the same way: each one targets a single censorship or blocking phenomenon, and a continuously-running measurement backend executes it on a schedule, accumulating data over time. We surveyed every Run v2 link to measure how concentrated this is, and to draw out what the pattern offers communities that want to run their own local connectivity observation.

OONI (the Open Observatory of Network Interference) is a global censorship-measurement project. Its mobile app, OONI Probe, runs through a list of websites and reports whether each one is reachable from where you are. OONI Run v2 lets anyone compose their own list of sites to watch, generate a link, and have others run that list with one tap in OONI Probe, with every result flowing into OONI's public dataset. You can define your own measurement targets without writing code, yet few people know the feature exists or have used it, which is exactly why we wanted to see how it is actually used.

What is Differential Privacy?

This article is based on the original explainer by fria at Privacy Guides:

Can you collect data from a large group of people while still protecting each individual's privacy? Differential privacy answers yes — with a mathematical proof to back it up. This article introduces the concept, traces its history from early anonymization failures to real-world deployments, and explores what it means for users and policymakers in Taiwan and the broader Chinese-speaking world.