Differential Privacy in Production

Overview

Differential privacy is a mathematical framework for quantifying and bounding the privacy loss incurred when publishing statistical information about a dataset. It provides a provable guarantee that the output of a computation does not significantly depend on whether any single individual's data is included. This skill covers the practical engineering of differential privacy systems for production deployment.

Core Definitions

(epsilon, delta)-Differential Privacy

A randomized mechanism M satisfies (epsilon, delta)-differential privacy if for all neighboring datasets D and D' (differing in at most one record) and for all possible outputs S:

P[M(D) in S] <= e^epsilon * P[M(D') in S] + delta

Where:

• epsilon (privacy loss budget): Quantifies the maximum information leakage. Lower epsilon = stronger privacy.
• delta: Probability of an additional privacy breach beyond the epsilon guarantee. Should be cryptographically small (< 1/n^2 where n is the dataset size).