Analytics/Archive/EventLogging/User agent sanitization

== Problem ==
User-agent strings often encode sufficiently many bits of information about the user's setup that they can be used to pinpoint an individual user, especially when
used in conjunction with other seemingly-anonymous datapoints. At the same time, user-agent strings are indispensable for performance measurements, front-end error reporting and browser support analysis. 
Historically, EventLogging left user-agent logging and processing up to the developers and analysts. The problem with this approach is that the sanitization of user-agent strings is not uniform. When it is done at all, it is often done inconsistently. This often ends up being a barrier for releasing performance-related datasets that would greatly benefit from additional scrutiny. 
The proposed solution is to centralize the processing of user-agents at the time of data collection.bugzilla:52295: Add sanitized User-Agent to default fields logged by EventLogging
 This way we can be confident that all UAs logged via EventLogging are adequately sanitized.
== Use cases for User Agent collection ==
Why do we need user agent data? 
To assess support needs. For example, we want to be sure we support every device with usage over x %, so we need to have device data reported with a precision of x.
Prioritizing Bug Fixes. In order to know whether a bug that affects a certain browser is a "must-fix" or a "nice to have" we need to have browser makeup stats. In both desktop and Mobile.
To plan feature work. We need to know, for example, the percentage of tablets among the users using wikipedia's mobile apps to plan application development. Or the number of users with "newer" 
browsers to plan work for Visual Editor.
To contextualize performance numbers. For any kind of performance work we do we need to have browser data available to target our efforts smartly. An example of a performance schema in EL: https://meta.wikimedia.org/wiki/Schema:NavigationTiming
== Fingerprinting: background ==
Entropy is the mathematical quantity that measures density of information, in this case it can be thought of how close a fact comes to revealing somebody's identity uniquely. Uniquely identifying a visiting user is referred to as "fingerprinting". When we learn a new fact about a person it reduces the entropy of their identity by a certain amount. Thus, the entropy of a user agent is the set of observable characteristics that can be used in concert with others to uniquely identify a user.
Entropy is normally measured in bits. For example, Peter Eckersley's Panopticlick study for the EFFhttps://www.eff.org/deeplinks/2010/01/tracking-by-user-agent finds that the User-Agent header provides about 10.0 bits of entropy. Since 2^10 == 1024 that means only 1 in 1024 random browsers visiting a site are expected to share the same user-agent header. Our goal when sanitizing user agents is to reduce the information the user agent provides about the user while still keeping enough information available to do performance diagnostics.
== Sanitization ==
The steps taken to further reduce the entropy of a user agent are quite simple: 
# We remove information pertaining to language first. These are headers like en-ES that are present in some user agents.
# We remove minor versions. For example: AppleWebkit/525.3.1 gets transformed to AppleWebkit/525
# We remove information regarding toolbars, extensions, plugins, builds, flash and Java when we find it. Disclaimer: these steps might include fully processing the user agent to identify the device, OS (major and minor) and browser (major). Pre processing UA has the advantage of filtering data such that we only store the data we care about. It, however, has the disadvantage to couple logging with a UA parsing solution. Also since data is pre processed we have lost the raw data that mistakes in the parsing library will not be easy to correct.
# To further sanitize the data any solution needs to incorporate bucketing and mark all UAs that haven't been encountered in the last N requests (for some appropriate value of N) into a generic "Other/unknown" bucket.
=== Caveats ===
== Aggregation ==
While sanitization reduces the amount of private information a User Agent contains it still leaves open the concern that you can pinpoint a browser session. The solution to this problem is reporting 
data in an aggregated fashion, thus discarding the original dataset and just leaving agreggates, like: "3% of users have an iPhone 5".
=== Caveats ===
Aggregation requires that you know before hand the reports you want to produce from the data. If original records are discharged it reduces your ability to explore the dataset.
== UA in EventLogging vs HTTP headers ==
It should be noted that the user-agent header is sent by the client by default regardless of what EventLogging explicitly logs in the EventCapsule: if we send a sanitized version of the UA string into the event query string on the client-side, it still gets sent in full with every request. Since we cannot prevent the client from sending raw UA headers, the current proposal is to make this data unavailable to any downstream subscriber of EventLogging data: we apply the canonicalization /sanitization as part of the parsing that precedes validation and broadcasting to subscribers. All the relevant EventLogging data consumers would be downstream relative to that. The current implementation doesn't perform any further transformation.
== References==
== Further reading ==
* https://www.eff.org/deeplinks/2010/01/primer-information-theory-and-privacy
* https://w3c.github.io/fingerprinting-guidance/
* http://panopticlick.eff.org/browser-uniqueness.pdf
* https://phabricator.wikimedia.org/diffusion/EEVL/browse/master/includes/EventLogging.php;f65dd5eedcc183c9a8a3319be56153209eba6221$61
Category:Data platform
Category:Data platform systems