I'm reading this article about how personal information that was anonymaized can usually be re identified. In the article at Theorem 3 the writers talk about
entropic deanonymization. I couldn't figure out what that is. So this is basically my question: What is entropic deanonymization?
Some context and background
Companies like Google/Facebook and many more publish their users data for many reasons, and to keep the private data private, the anonymize the data (remove names, id numbers, address etc.). So then they claim that no ones privacy is harmed. This article say's mainly that it's very compicated to really anonymize the data. So the paper talks about the relation about how much data is published and how much "deanonymization power" you get, Theorem 1 makes the first binding between
m the amount of data published and how good the dataset can be deanonymized. Theorem 2 talks about the same when you have a sparse dataset, like most real world datasets. Theorem 3 is talking about entropy somehow, and this is where is started wondering what
entropic deanonymization is. Later they talk about what you do when only a subset is published (after anonymization) and later on they show results from the Netflix prize - a real competition where this technique was used.
Just for clarity: The author didn't use the phrase
entropic deanonymization. It's a semantic conversion I did, because it was never given a name. Sorry if that's misleading as pointed out in the comments. So my question rephrased: What entropy has to do with deanonymization?