Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds

Understand

"Concentrated differential privacy" was recently introduced by Dwork and Rothblum as a relaxation of differential privacy, which permits sharper analyses of many privacy-preserving computations.

• We present an alternative formulation of the concept of concentrated differential privacy in terms of the Renyi divergence between the distributions obtained by running an algorithm on neighboring inputs.
• With this reformulation in hand, we prove sharper quantitative results, establish lower bounds, and raise a few new questions.
• We also unify this approach with approximate differential privacy by giving an appropriate definition of "approximate concentrated differential privacy."