In this post, I want to briefly introduce Differential Privacy to you, which, in my honest opinion, needs to get more attention in the software developer community.
During my Master thesis, I evaluated the use of Differential Privacy for Federated Learning (I might explain Federated Learning in another post).
The Theory Differential Privacy, originally $\epsilon$-Differential Privacy (DP)1, is a way to secure the privacy of individuals in a statistical database. A statistical database is a database, where only aggregation functions like “sum”, “average”, “count”, et cetera… can be executed.
Read more