On Distributed Differential Privacy and Counting Distinct Elements

09/21/2020 ∙ by Lijie Chen, et al. ∙ 5

We study the setup where each of n users holds an element from a discrete set, and the goal is to count the number of distinct elements across all users, under the constraint of (ϵ, δ)-differentially privacy: - In the non-interactive local setting, we prove that the additive error of any protocol is Ω(n) for any constant ϵ and for any δ inverse polynomial in n. - In the single-message shuffle setting, we prove a lower bound of Ω(n) on the error for any constant ϵ and for some δ inverse quasi-polynomial in n. We do so by building on the moment-matching method from the literature on distribution estimation. - In the multi-message shuffle setting, we give a protocol with at most one message per user in expectation and with an error of Õ(√(()n)) for any constant ϵ and for any δ inverse polynomial in n. Our protocol is also robustly shuffle private, and our error of √(()n) matches a known lower bound for such protocols. Our proof technique relies on a new notion, that we call dominated protocols, and which can also be used to obtain the first non-trivial lower bounds against multi-message shuffle protocols for the well-studied problems of selection and learning parity. Our first lower bound for estimating the number of distinct elements provides the first ω(√(()n)) separation between global sensitivity and error in local differential privacy, thus answering an open question of Vadhan (2017). We also provide a simple construction that gives Ω̃(n) separation between global sensitivity and error in two-party differential privacy, thereby answering an open question of McGregor et al. (2011).

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.