I conclude my mini-mini-series with discussion of differential privacy, its imposing mathematical theory on Wikipedia vs. the relatively prosaic implementation reality of simply adding noise, why adding noise provides more privacy than you might think, and how these methods can go bad when used on datasets where outliers are numerous, extreme, or important.