June 20, 2021

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Differentially Private Histograms in the Shuffle Model from Fake Users. (arXiv:2104.02739v3 [cs.CR] UPDATED)

There has been much recent work in the shuffle model of differential privacy,
particularly for approximate $d$-bin histograms. While these protocols achieve
low error, the number of messages sent by each user — the message complexity
— has so far scaled with $d$ or the privacy parameters. The message complexity
is an informative predictor of a shuffle protocol’s resource consumption. We
present a protocol whose message complexity is two when there are sufficiently
many users. The protocol essentially pairs each row in the dataset with a fake
row and performs a simple randomization on all rows. We show that the error
introduced by the protocol is small, using rigorous analysis as well as
experiments on real-world data. We also prove that corrupt users have a
relatively low impact on our protocol’s estimates.