Data Publishing
Introduction
Mozilla’s history is steeped in openness and transparency - it’s simply core to what we do and how we see ourselves in the world. We are always looking for ways to bring our mission to life in ways that help create a healthy internet and support the Mozilla Manifesto. One of our commitments says “We are committed to an internet that elevates critical thinking, reasoned argument, shared knowledge, and verifiable facts”.
To this end, we have spent a good amount of time considering how we can publicly share our Mozilla telemetry data sets - it is one of the most simple and effective ways we can enable collaboration and share knowledge. But, only if it can be done safely and in a privacy protecting, principled way. We believe we’ve designed a way to do this and we are excited to outline our approach here.
Dataset Publishing Process
We want our data publishing review process, as well as our review decisions to be public and understandable, similar to our Mozilla Data Collection program. To that end, our full dataset publishing policy and details about what considerations we look at before determining what is safe to publish can be found below, including asummary of the critical pieces of that process.
The goal of our data publishing process is to:
- Reduce friction for data publishing requests with low privacy risk to users;
- Have a review system of checks and balances that considers both data aggregations and data level sensitivities to determine privacy risk prior to publishing, and;
- Create a public record of these reviews, including making data and the queries that generate it publicly available and putting a link to the dataset + metadata on a public-facing Mozilla property.
This page defines all of the factors that must be taken into consideration before publicly sharing Mozilla’s telemetry data. It describes:
- The levels of possible dataset aggregations using Mozilla’s data
- The levels of publishing sensitivity
- What dimensions are sensitive, and at which level
- What metrics are sensitive, and at which level
- How we characterize the levels of aggregation
How we characterize the levels of aggregation The table below describes the various types of aggregation levels we are defining.
| Level | Aggregation | Examples |
|---|---|---|
| 1 | Statistical / ML Models | TAAR, Federated learning models, Forecasting models |
| 2 | Dimension-level aggregation w/ minimum bucket sizes | Total page loads by country, OS, locale, channel where any combination with a count less than 5,000 are grouped into “Other”
[Canada, Linux, “Other locales”, nightly] for rare locales |
| 3 | Dimension-level aggregation w/o minimum bucket sizes | Clientid count by country, os, locale, channel, where there could be: [Canada, Linux, PL, nightly] which has one client in it. |
| 4 | Probabilistic Aggregates | HLL for computing approximate unique client counts, bloom filter for computing presence in a set |
| 5 | Anonymized individual-level data |
|
| 6 | Not-anonymized individual-level data |
|
| 7 | High resolution individual-level data | Raw telemetry events data, a sequence of actions in order of occurrence. |