(This article was originally published at Statistics – John D. Cook, and syndicated at StatsBlogs.)

It can be surprisingly easy to identify someone from data that’s not directly identifiable. One commonly cited result is that the combination of birth date, zip code, and sex is enough to identify 87% of Americans. This post will look at how to quantify the amount of information contained in such data.

If the answer to a question has probability *p*, then it contains -log_{2} *p* **bits of information**. Knowing someone’s sex gives you 1 bit of information because -log_{2}(1/2) = 1.

Knowing whether someone can roll their tongue could give you more or less information than knowing their sex. Estimates vary, but say 75% can roll their tongue. Then knowing that someone *can* roll their tongue gives you 0.415 bits of information, but knowing that they *cannot* roll their tongue gives you 2 bits of information.

On *average*, knowing someone’s tongue rolling ability gives you less information than knowing their sex. The average amount of information, or **entropy**, is

0.75(-log_{2} 0.75) + 0.25(-log_{2} 0.25) = 0.81.

Entropy is maximized when all outcomes are equally likely. But for identifiability, we’re concerned with maximum information as well as average information.

Knowing someone’s zip code gives you a variable amount of information, less for densely populated zip codes and more for sparsely populated zip codes. An average zip code contains about 7,500 people. If we assume a US population of 326,000,000, this means a typical zip code would give us about 15.4 bits of information. (Update: see a more precise calculation here.)

The Safe Harbor provisions of US HIPAA regulations let you use the first three digits of someone’s zip code except when this would represent less than 20,000 people, as it would in several sparsely populated areas. Knowing that an American lives in a region of 20,000 people would give you 14 bits of information about that person.

Birth dates are complicated because age distribution is uneven. Knowing that someone’s birth date was over a century ago is highly informative, much more so than knowing it was a couple decades ago. That’s why the Safe Harbor provisions do not allow including age, much less birth date, for people over 90.

Birthdays are simpler than birth dates. Birthdays are not perfectly evenly distributed throughout the year, but they’re close enough for our purposes. If we ignore leap years, a birthday contains -log_{2}(1/365) or about 8.5 bits of information. If we consider leap years, knowing someone was born on a leap day gives us two extra bits of information.

Independent information is additive. I don’t expect there’s much correlation between sex, geographical region, and birthday, so you could add up the bits from each of these information sources. So if you know someone’s sex, their zip code (assuming 7,500 people), and their birthday (not a leap day), then you have 25 bits of information, which may be enough to identify them.

This post didn’t consider correlated information. For example, suppose you know someone’s zip code and primary language. Those two pieces of information together don’t provide as much information as the sum of the information they provide separately because language and location are correlated. I may discuss the information content of correlated information in a future post. (**Update**: Here is a post on correlated pairs of data.)

**Related**: HIPAA de-identification

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