Protecting Privacy with MATH (Collab with the Census)

> That would make the existence of anonymous data practically speaking impossible to have on the web For almost every type of data that is true. Transforming or substituting data doesn't make it anonymous; the patters in the data are still present. To produce actually anonymous data you have to do what the GDPR instructed: corrupt the data ("rendered anonymous") severely enough that the "data subject is ... no longer identifiable". You need to do something like aggregate the data into a small number of groups such that individual records no longer exist. Techniques like "differential privacy" let you control precisely how "anonymous" your data is by e.g. mixing in carefully crafted noise. > 8192 bucket While others have pointed out that this isn't actually limited to 13 bits of entropy for most people, there are at least two reasons that field is still very personally identifying. First, "x-client-data on its own" never happens. Google isn't wasting time and money implementing this feature to make an isolated database with a single column. At no point will the x-client-data value (or any other type of data they capture) ever sit in isolation. I used the IPv4 Source Address as an example because it will necessarily be present in the header of the packets that transport the x-client-data header over the internet. Suggesting that Google would ever use this value in isolation is almost insulting to Google; why would they waste their expensive developer time to create, capture, an

Your comment reminded me of this minutephysics video about the census. [VIDEO] It a great walkthrough of the "privacy doom principle."

Technical explanation by minutephysics: [VIDEO] The amount of privacy is configurable, and researchers who would like to tune it (in either direction) can make a plea so the public can decide the amount of privacy loss they're willing give up in order to aid research. Each culture is different and has different tolerances for privacy and amount of intrusiveness.