Differential privacy - Explained

The first time I heard the term "differential privacy" it was in Apple marketing on how they were protecting privacy but still getting information to improve usability. However, I didn’t know specifically what it meant. Here is a good definition.

Differential privacy (DP) works by adding a small amount of noise (see Laplace Mechanism) to the result of our query (e.g. a bit of noise to a mean query result). This process guarantees that one person's inclusion or exclusion in the data barely impacts the results, preventing the identification of individual information from the combined data. In other words, if you were to remove one individual from the dataset, you would get the same end result.

There are two approaches to DP:

• Local DP: Adding noise to the data itself (Apple Laplace Mechanism for example).
• Central DP: Adding noise to query results (what most databases/libraries do).

Tons of links to other articles about this technique as well as various libraries.