Ted is writing things
Differential privacy is getting a lot of attention lately. Companies and governments are starting to publish data anonymized with this notion. Universities are offering courses about it. Statisticians are getting acquainted with this new approach to protecting data. Open-source organizations are publishing tooling to make differential privacy easier to use.
So, you might be wondering: what's the hype all about? What even is differential privacy? What makes it so special? How does it work in practice? And, perhaps more importantly, can I understand it without having to read a bunch of complicated equations?
The good news is: you've come to the right place. Welcome to my friendly blog post series about differential privacy! It provides simple explanations for the core concepts behind differential privacy. It is meant for a wide, non-technical audience: it doesn't assume any prior knowledge, uses as little math as possible, and illustrates everything with simple examples and diagrams.
Same diagram as before, duplicated, with the bottom line missing one person in the database. A double arrow labeled "basically the same" points to the two outputs. Same diagram as before, duplicated, with the bottom line missing one person in the database. A double arrow labeled "basically the same" points to the two outputs. image/svg+xml Some process
