Relaxed Marginal Consistency for Differentially Private Query Answering

Ryan McKenna, Siddhant Pradhan, Daniel Sheldon, Gerome Miklau

Keywords: differential privacy, convex optimization, graphical models, approximate inference, local polytope

TL;DR: We propose a post-processing technique that boosts utility by enforcing (local) consistency constraints. Our method is scalable to far more general settings than prior work.

Abstract:

Many differentially private algorithms for answering database queries involve a
step that reconstructs a discrete data distribution from noisy measurements. This
provides consistent query answers and reduces error, but often requires space that
grows exponentially with dimension. PRIVATE-PGM is a recent approach that uses
graphical models to represent the data distribution, with complexity proportional to
that of exact marginal inference in a graphical model with structure determined by
the co-occurrence of variables in the noisy measurements. PRIVATE-PGM is highly
scalable for sparse measurements, but may fail to run in high dimensions with dense
measurements. We overcome the main scalability limitation of PRIVATE-PGM
through a principled approach that relaxes consistency constraints in the estimation
objective. Our new approach works with many existing private query answering
algorithms and improves scalability or accuracy with no privacy cost.

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Code: https://github.com/ryan112358/private-pgm/tree/approx-experiments-snapshot