Poster
Anonymized Histograms in Intermediate Privacy Models
Badih Ghazi · Pritish Kamath · Ravi Kumar · Pasin Manurangsi
Hall J (level 1) #829
Keywords: [ pan privacy ] [ shuffle DP ] [ anonymized histograms ] [ differential privacy ]
Abstract:
We study the problem of privately computing the \it anonymized histogram\it anonymized histogram (a.k.a. \it unattributed histogram), which is defined as the histogram without item labels. Previous works have provided algorithms with ℓ1- and ℓ22-errors of Oε(√n) in the central model of differential privacy (DP).In this work, we provide an algorithm with a nearly matching error guarantee of ˜Oε(√n) in the shuffle DP and pan-private models. Our algorithm is very simple: it just post-processes the discrete Laplace-noised histogram! Using this algorithm as a subroutine, we show applications in privately estimating symmetric properties of distributions such as entropy, support coverage, and support size.
Chat is not available.