IncA is a fully decentralized, differentially private mean-estimation protocol whose correlated noise cancels in the no-dropout case, achieving central-DP accuracy under a strong adversarial model.
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Dropout-Robust Mechanisms for Differentially Private and Fully Decentralized Mean Estimation
IncA is a fully decentralized, differentially private mean-estimation protocol whose correlated noise cancels in the no-dropout case, achieving central-DP accuracy under a strong adversarial model.