4 ms·

I’m sorry but I don’t buy that. From a practical stand point, what am I going to do if you hand me a sex, birthdate and zip code and tell me to find who owns th

I’m sorry but I don’t buy that. From a practical stand point, what am I going to do if you hand me a sex, birthdate and zip code and tell me to find who owns them? I would have to talk to every person in that zip code who matched the sex (have to account for the possibility two people have the same birthday). At that point, I’m getting better records pounding the streets than I am from some database.

kccqzy 3y ago
A key insight from anonymization is that you need to consider how easy it is to join that dataset together with some other freely available or cheaply available datasets. GP said the person spent $20 buying voter information. A moderate to big company will even prevent employees from joining data sets from different departments of the company, as a matter of policy.
Sai_ 3y ago
Knowing gender cuts down your sample space by half. Assuming you are looking at someone between the ages of 1-80, knowing birthdate further filters in just 1÷(80 * 365) of the sample space. Since they're 42000 ZIP codes in the US, knowing the ZIP code lets you filter in just 1÷42000 of the sample space. Together, 1÷2 x 1÷(80*365) x 1÷42000 = 0.00000041% With these three datapoints, you can identify roughly 1.3 US persons (assuming a US population of 330M). Not too bad, imho.
- fingerlocks 3y ago
  Only if age, birthdate, and zip codes are uniformly distributed among the population. Which they aren’t. At all.
  - Sai_ 3y ago
    Here's what I found for birthdates - https://www.panix.com/~murphy/bday.html https://www.panix.com/~murphy/bday.html. The variation between dates doesn't seem all that bad. So, for all practical purposes, we can assume that births across dates is uniform. Given this, the only real issue is ZIP codes. If we assume that we know nothing about how populations are distributed across ZIP codes, given just the gender and date of birth, we can narrow down the cohort to just 5650 US persons (330M x 1/2 x 1/(80x365)). According to this link - https://www.johndcook.com/blog/2019/08/21/zip-code-population/ https://www.johndcook.com/blog/2019/08/21/zip-code-populatio... - 80% of the US population lives in 27% of her ZIP codes. Assuming your target individual is in the 80%, given the gender, birthdate, and ZIP code, you can narrow down to the following - 0.8x330M x 1/2 x 1/(80x365) x 1/(0.27x42000) = 0.4 US persons per ZIP code. Basically, these three data points can almost certainly uniquely identify specific individuals - the only remaining thing is to connect a name/phone number to each individual.
    - fingerlocks 3y ago
      Obviously the data is not really anonymized. Im just nitpicking your weirdly precise results of your fermi math. It would be easier to grab this from the census data, right? 40 year old males with a given birthdate, no zip code, narrows to ~7,154.

kccqzy 3y ago

A key insight from anonymization is that you need to consider how easy it is to join that dataset together with some other freely available or cheaply available datasets. GP said the person spent $20 buying voter information. A moderate to big company will even prevent employees from joining data sets from different departments of the company, as a matter of policy.

Sai_ 3y ago

Knowing gender cuts down your sample space by half. Assuming you are looking at someone between the ages of 1-80, knowing birthdate further filters in just 1÷(80 * 365) of the sample space. Since they're 42000 ZIP codes in the US, knowing the ZIP code lets you filter in just 1÷42000 of the sample space. Together, 1÷2 x 1÷(80*365) x 1÷42000 = 0.00000041% With these three datapoints, you can identify roughly 1.3 US persons (assuming a US population of 330M). Not too bad, imho.

fingerlocks 3y ago
Only if age, birthdate, and zip codes are uniformly distributed among the population. Which they aren’t. At all.
- Sai_ 3y ago
  Here's what I found for birthdates - https://www.panix.com/~murphy/bday.html https://www.panix.com/~murphy/bday.html. The variation between dates doesn't seem all that bad. So, for all practical purposes, we can assume that births across dates is uniform. Given this, the only real issue is ZIP codes. If we assume that we know nothing about how populations are distributed across ZIP codes, given just the gender and date of birth, we can narrow down the cohort to just 5650 US persons (330M x 1/2 x 1/(80x365)). According to this link - https://www.johndcook.com/blog/2019/08/21/zip-code-population/ https://www.johndcook.com/blog/2019/08/21/zip-code-populatio... - 80% of the US population lives in 27% of her ZIP codes. Assuming your target individual is in the 80%, given the gender, birthdate, and ZIP code, you can narrow down to the following - 0.8x330M x 1/2 x 1/(80x365) x 1/(0.27x42000) = 0.4 US persons per ZIP code. Basically, these three data points can almost certainly uniquely identify specific individuals - the only remaining thing is to connect a name/phone number to each individual.
  - fingerlocks 3y ago
    Obviously the data is not really anonymized. Im just nitpicking your weirdly precise results of your fermi math. It would be easier to grab this from the census data, right? 40 year old males with a given birthdate, no zip code, narrows to ~7,154.