Designing Privacy-Preserving Analytics

Overview

Privacy-preserving analytics enables organizations to extract statistical insights from personal data without exposing individual-level information. This directly supports GDPR Article 5(1)(c) (data minimization) and Recital 26 (which exempts truly anonymous data from the regulation). The Article 29 Working Party Opinion 05/2014 on Anonymisation Techniques (WP216) established that effective anonymization must resist singling out, linkability, and inference attacks.

Four primary statistical disclosure control techniques form the foundation of privacy-preserving analytics: differential privacy, k-anonymity, l-diversity, and t-closeness. Each offers different trade-offs between privacy guarantees and data utility.

Statistical Disclosure Control Techniques

Differential Privacy

Differential privacy (Dwork et al., 2006) provides a mathematical guarantee that the output of an analysis is approximately the same whether or not any single individual's data is included. Formally, a randomized mechanism M satisfies (epsilon, delta)-differential privacy if for all datasets D1 and D2 differing in at most one record, and for all sets of outputs S:

P[M(D1) ∈ S] ≤ e^ε × P[M(D2) ∈ S] + δ

Where epsilon (ε) is the privacy loss parameter and delta (δ) bounds the probability of privacy breach.