ooni-probe - Open Observatory of Network Interference

"The Net interprets censorship as damage and routes around it."
                - John Gilmore; TIME magazine (6 December 1993)

The goal of ooni-probe is to collect data about censorship around
the world.

OONI uses open methodologies and the data will be provided in raw
format to allow any researcher to indipendently draw their conclusions
from the results OONI tests.

There are currently projects aimed at measuring censorship in one
way or another but they either use non open methodologies or their
tools are not open sources. OONI aims at filling up this gap by
creating the first open source framework for developing network
tests and collecting data on censorship.

OONI revolves around three major concepts: Assets, Tests and
Reports.

# Assets

Assets are the inputs used inside Tests to detect censorship events.
These can be URL lists, keywords, ip addresses, packets or any kind
of set of data.
In the python specific implementation this is represented as a python
iterable object. This means that the Testing framework will be able
to iterate through every element in the Asset.

# Tests

This is the core of OONI. These are the actual tests that will be run
using as input (if an input is required) the Assets.
Tests can be summarized as an experiment and a control. The control
represents the expected result and the experiment is the network operation
being performed on the live network. If the experiment does not match up
with the control then a censorship event had occured.

OONI probe provides some useful functionality to the application developer
that may be useful when developing censorship detection tests. For example
it is possible to make a request over the Tor network easily or use a fast
and flexible non-blocking HTTP client implementation.

# Reports

This is the data that is collected from the test. OONI probe provides a
flexible means of storing results and uploading this data to a remote
server or a flat file.

The Test developer should include in the report as much data as possible
and can contain raw packet dumps as well as structured synthetic results.

In future on top of ooni-probe Reports it will be possible to develop
flexible post-processing tools to allow data-visualization guru's to
properly visualize and contextualize the resulting data.