Private Aggregation from Fewer Anonymous Messages
Badih Ghazi, Pasin Manurangsi, Rasmus Pagh, Ameya Velingker
Abstract
Consider the setup where n parties are each given an element minimal amsmath wasysym amsfonts amssymb amsbsy mathrsfs upgreek -69pt documentdocumentxi in the finite field minimal amsmath wasysym amsfonts amssymb amsbsy mathrsfs upgreek -69pt documentdocumentFq and the goal is to compute the sum minimal amsmath wasysym amsfonts amssymb amsbsy mathrsfs upgreek -69pt documentdocument∑ixi in a secure fashion and with as little communication as possible. We study this problem in the anonymized model of Ishai et al. (FOCS 2006) where each party may broadcast anonymous messages on an insecure channel. We present a new analysis of the one-round “split and mix” protocol of Ishai et al. In order to achieve the same security parameter, our analysis reduces the required number of messages by a minimal amsmath wasysym amsfonts amssymb amsbsy mathrsfs upgreek -69pt documentdocumentΘ(logn) multiplicative factor. We also prove lower bounds showing that the dependence of the number of messages on the domain size, the number of parties, and the security parameter is essentially tight. Using a reduction of Balle et al. (2019), our improved analysis of the protocol of Ishai et al. yields, in the same model, an minimal amsmath wasysym amsfonts amssymb amsbsy mathrsfs upgreek -69pt documentdocumentε,δ-differentially private protocol for aggregation that, for any constant minimal amsmath wasysym amsfonts amssymb amsbsy mathrsfs upgreek -69pt documentdocumentε>0 and any minimal amsmath wasysym amsfonts amssymb amsbsy mathrsfs upgreek -69pt documentdocumentδ=1poly(n), incurs only a constant error and requires only a constant number of messages per party. Previously, such a protocol was known only for minimal amsmath wasysym amsfonts amssymb amsbsy mathrsfs upgreek -69pt documentdocumentΩ(logn) messages per party.
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Cited by top-tier papers22
- The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure AggregationPeter Kairouz, Ziyu Liu, Thomas SteinkeICML 2021 · 291 citations
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- Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication OverheadBadih Ghazi, Ravi Kumar, Pasin Manurangsi, Rasmus PaghICML 2020 · 59 citations
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- Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single MessageBadih Ghazi, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh et al.ICML 2021 · 45 citations
Builds on4
- Deep Learning with Differential PrivacyMartín Abadi, Andy Chu, Ian J. Goodfellow, H. Brendan McMahan et al.CCS 2016 · 7,620 citations
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- Improving Utility and Security of the Shuffler-based Differential PrivacyTianhao Wang, Min Xu, Bolin Ding, Jingren Zhou et al.VLDB 2020 · 39 citations
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