Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message
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The shuffle model of differential privacy has attracted attention in the literature due to it being a middle ground between the well-studied central and local models. In this work, we study the problem of summing (aggregating) real numbers or integers, a basic primitive in numerous machine learning tasks, in the shuffle model. We give a protocol achieving error arbitrarily close to that of the (Discrete) Laplace mechanism in central differential privacy, while each user only sends 1 + o(1) short messages in expectation.
Original language | English |
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Title of host publication | Proceedings of the 38 th International Conference on Machine Learning |
Editors | M Meila, T Zhang |
Publisher | PMLR |
Publication date | 2021 |
Pages | 3692-3701 |
Publication status | Published - 2021 |
Event | 38th International Conference on Machine Learning (ICML) - Virtual Duration: 18 Jul 2021 → 24 Jul 2021 |
Conference
Conference | 38th International Conference on Machine Learning (ICML) |
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By | Virtual |
Periode | 18/07/2021 → 24/07/2021 |
Series | Proceedings of Machine Learning Research |
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Volume | 139 |
ISSN | 2640-3498 |
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Links
- https://proceedings.mlr.press/v139/
Final published version
ID: 301135973