title: Relaxed Marginal Consistency for Differentially Private Query Answering
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.
authors: Ryan McKenna Siddhant Pradhan Daniel Sheldon Gerome Miklau
hIndex: Gerome Miklau 37