<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>rzwitserloot</author><text>I&amp;#x27;m a programmer in the GP data analysis world. We use the term &amp;#x27;pseudonymization&amp;#x27; for this kind of data. &amp;#x27;Anonymization&amp;#x27; is used solely to refer to, say, &amp;#x27;the sum total of diabetes patients this practice has&amp;#x27; (that would be anonymous patient data; it would not be anonymous relative to the GP office this refers to): Aggregated data that can no longer be reduced to a single individual at all.&lt;p&gt;The term raises questions: Okay, so, what does it mean? How &amp;#x27;pseudo&amp;#x27; is psuedo? And that&amp;#x27;s the point: When you pseudonimize data, you must ask those questions and there is no black and white anymore.&lt;p&gt;My go-to example to explain this is very simple: Let&amp;#x27;s say we reduce birthdate info to just your birthyear, and geoloc info to just a wide area. And then I have an pseudonimized individual who is marked down as being 105 years old.&lt;p&gt;Usually there&amp;#x27;s only one such person.&lt;p&gt;I invite everybody who works in this field to start using the term &amp;#x27;pseudonimization&amp;#x27;.</text><parent_chain></parent_chain></comment><story><title>Algorithm can pick out almost any American in supposedly anonymized databases</title><url>https://www.nytimes.com/2019/07/23/health/data-privacy-protection.html</url></story>
<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>shusson</author><text>The article title misses a bit of nuance from the paper which is specifically talking about re-identification.&lt;p&gt;e.g from the paper:&lt;p&gt;&amp;quot;We show that, as a male born on July 31, 1945 and living in Cambridge (02138), the information used by Latanya Sweeney at the time, William Weld was unique with a 58% likelihood (ξx = 0.58 and κx = 0.77), meaning that Latanya Sweeney’s re-identification had 77% chances of being correct. We show that, if his medical records had included number of children—5 for William Weld—, her re-identification would have had 99.8% chances of being correct!&amp;quot;</text><parent_chain></parent_chain></comment><story><title>Algorithm can pick out almost any American in supposedly anonymized databases</title><url>https://www.nytimes.com/2019/07/23/health/data-privacy-protection.html</url></story>