PostDPD

PostDPD is a term used in privacy-preserving data analysis to denote a family of post-processing techniques applied to outputs produced by differential privacy mechanisms in order to improve utility while preserving privacy guarantees. The central idea is that after a DP mechanism releases noisy statistics, additional processing can enforce internal consistency, reduce error, or align results with known constraints, without weakening the original privacy guarantees due to the DP post-processing theorem.

Techniques commonly include constrained optimization to enforce consistency across correlated statistics, denoising and smoothing within DP-compatible bounds, calibration against external data or known marginals, and synthetic data generation that preserves the DP properties of the source outputs. These methods typically operate on the released DP data rather than on raw data, and they rely on domain knowledge to guide improvements.

Applications span public-sector statistics, healthcare analytics, and location-based services, where agencies and researchers seek higher utility from noisy releases such as counts, contingency tables, and summary statistics. PostDPD is especially relevant for multi-source releases, where cross-statistical consistency is desirable.

Evaluation focuses on utility metrics (mean squared error, bias, KL divergence) and fidelity to known constraints, while ensuring fairness and avoiding amplification of biases in external data. Limitations include sensitivity to biased external information and the risk that aggressive post-processing may obscure data provenance or complicate audit trails.

See also differential privacy, post-processing theorem, synthetic data.