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Differential Privacy in the Shuffle Model: A Survey of Separations
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Differential privacy is often studied in one of two models. In the central model, a single analyzer has the responsibility of performing a privacy-preserving computation on data. But in the local model, each data owner ensures their own privacy. Although it removes the need to trust the analyzer, local privacy comes at a price: a locally private protocol is less accurate than a centrally private counterpart when solving many learning and estimation problems. Protocols in the shuffle model are designed to attain the best of both worlds: recent work has shown high accuracy is possible with only a mild trust assumption. This survey paper gives an overview of novel shuffle protocols, along with lower bounds that establish the limits of the new model. We also summarize work that show the promise of interactivity in the shuffle model.
Forward citations
Cited by 3 Pith papers
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Lightweight Protocols for Distributed Private Quantile Estimation
Adaptive local privacy can estimate any quantile over a domain of size B with O(log B/(epsilon^2 alpha^2)) users, which is optimal and a log B factor better than nonadaptive protocols.
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Network-Aware Differential Privacy
Network-Aware Differential Privacy initiates a research agenda connecting networking and differential privacy, with preliminary evidence that packet-level adversaries can manipulate local DP protocols.
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