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Differential privacy (DP) is a formal notion for quantifying the privacy lossof algorithms. Algorithms in the central model of DP achieve high accuracy butmake the strongest trust assumptions whereas those in the local DP model makethe weakest trust assumptions but incur substantial accuracy loss. The shuffledDP model (Bittau et al., 2017; Erlingsson et al., 2019; Cheu et al., 2019) hasrecently emerged as a feasible middle ground between the central and localmodels, providing stronger trust assumptions than the former while promisinghigher accuracies than the latter. In this paper, we obtain practicalcommunication-efficient algorithms in the shuffled DP model for two basicaggregation primitives used in machine learning: 1) binary summation, and 2)histograms over a moderate number of buckets. Our algorithms achieve accuracythat is arbitrarily close to that of central DP algorithms with an expectedcommunication per user essentially matching what is needed without any privacyconstraints! We demonstrate the practicality of our algorithms byexperimentally comparing their performance to several widely-used protocolssuch as Randomized Response (Warner, 1965) and RAPPOR (Erlingsson et al.,2014).

Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead