Simulating identification by zip code, sex, and birthdate
While this is intended as just a proof of concept, I think this technique is mathematically flawed.  You can&#x27;t just use the average number of people in each zipcode, because if there is great variation in the number of people per zipcode, a randomly sampled person is more likely to be in a &quot;large&quot; zipcode than a small one.<p>Consider the case where we have 100000 people, and 10 zipcodes, each with an average of 10000 people.  Now add that zipcodes 0-8 have only one person, and zipcode 9 has the remaining 99991 people.    What percentage of Americans can be reliably identified by sex, zipcode, and full birthdate?<p>I don&#x27;t know the exact answer, but I&#x27;m pretty sure it&#x27;s closer to .01% than 87%.  The 9 people in the 9 small districts are reliably identifiable, and 99.99% of the population in the large 10th district are essentially not identifiable.   While the actual population distribution by zipcode is surely not this extreme, I think this example shows that the question cannot be answered unless a specific population distribution is known (or assumed).<p>Actual distribution of population can be seen here: <a href="http:&#x2F;&#x2F;proximityone.com&#x2F;zip-place.htm" rel="nofollow">http:&#x2F;&#x2F;proximityone.com&#x2F;zip-place.htm</a> (click on Population twice to sort by most populous).  At a glance, there are quite a few zipcodes with far more than 10,000 people, making me think this is going to skew the results considerably.