Achieving Differential Privacy in Secure Multiparty Data Aggregation Protocols on Star Networks

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Honeycrisp: large-scale differentially private aggregation without a trusted core (2019, background): “However, [12] requires each user to perform O(n) public-key encryptions and requires O(n) communication between each user and the aggregator, and thus is not suitable in our setting, where n is very large, possibly up to a billion.”
Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization (2018, background): “Recent works on distributed noise generation [17, 3, 24, 45] try to achieve a similar bound by requiring parties to add partial noise locally, and combining these noises to ensure differential privacy.”
$\textsf{LoPub}$ : High-Dimensional Crowdsourced Data Publication With Local Differential Privacy (2016, methodology): “[8] use noise partitioning technique to mimic the situation that the Laplace-distributed noise sample applies to the aggregate data.”