Concurrent Shuffle Differential Privacy Under Continual Observation

Jay Tenenbaum*, Haim Kaplan*, Yishay Mansour*, Uri Stemmer*

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review


We introduce the concurrent shuffle model of differential privacy. In this model we have multiple concurrent shufflers permuting messages from different, possibly overlapping, batches of users. Similarly to the standard (single) shuffle model, the privacy requirement is that the concatenation of all shuffled messages should be differentially private. We study the private continual summation problem (a.k.a. the counter problem) and show that the concurrent shuffle model allows for significantly improved error compared to a standard (single) shuffle model. Specifically, we give a summation algorithm with error Õ(n1/(2k+1)) with k concurrent shufflers on a sequence of length n. Furthermore, we prove that this bound is tight for any k, even if the algorithm can choose the sizes of the batches adaptively. For k = log n shufflers, the resulting error is polylogarithmic, much better than Θ̃(n1/3) which we show is the smallest possible with a single shuffler. We use our online summation algorithm to get algorithms with improved regret bounds for the contextual linear bandit problem. In particular we get optimal Õ(√n) regret with k = Ω̃(log n) concurrent shufflers.

Original languageEnglish
Pages (from-to)33961-33982
Number of pages22
JournalProceedings of Machine Learning Research
StatePublished - 2023
Event40th International Conference on Machine Learning, ICML 2023 - Honolulu, United States
Duration: 23 Jul 202329 Jul 2023


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