Protecting Privacy with MATH (Collab with the Census)
Why Hacker News recommends it
Linked as the clearest technical explainer of differential privacy and the census, and of why aggregate data can still compromise privacy.
What it is
minutephysics, in collaboration with the US Census Bureau, explains differential privacy and why the 2020 census adds noise to protect individuals' data. It walks through how statistics can leak private information.
Recommended by 2 HN commenters across 2 different months, 2020 to 2022.
What Hacker News said
Recommends Zacru · 2021-04 · on “Cohort IDs can be collected over time to create cross-site tracking IDs”
Your comment reminded me of this minutephysics video about the census. [this video] It a great walkthrough of the "privacy doom principle."
Recommends achille · 2022-03 · on “Why the 2020 census has 9 fake people in a single house”
Technical explanation by minutephysics: [this 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.
Cites pdkl95 · 2020-02 · on “Google tracks individual users per Chrome installation ID”
> 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. >