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Poster
Anonymized Histograms in Intermediate Privacy Models
Badih Ghazi · Pritish Kamath · Ravi Kumar · Pasin Manurangsi

Tue Nov 29 02:00 PM -- 04:00 PM (PST) @ Hall J #829
in Poster Session 2 »
We study the problem of privately computing the $\mbox{\it anonymized histogram}$ (a.k.a. $\mbox{\it unattributed histogram}$), which is defined as the histogram without item labels. Previous works have provided algorithms with $\ell_1$- and $\ell_2^2$-errors of $O_\varepsilon(\sqrt{n})$ in the central model of differential privacy (DP).In this work, we provide an algorithm with a nearly matching error guarantee of $\widetilde{O}_\varepsilon(\sqrt{n})$ 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.
Keywords: differential privacy · pan privacy · shuffle DP · anonymized histograms
[ Paper]  [ Poster]  [ OpenReview] 

Author Information

Badih Ghazi (Google)
Pritish Kamath (Google Research)
Ravi Kumar (Google)
Pasin Manurangsi (Google)

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