Private Aggregation from Fewer Anonymous Messages
Not on the map. Why?
Abstract (arXiv)
Consider the setup where n parties are each given a number xi ∈ mathbbFq and the goal is to compute the sum sumi xi 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 Theta(log n) multiplicative factor. We complement our positive result with 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 left(varepsilon, δright)-differentially private protocol for aggregation that, for any constant varepsilon > 0 and any δ = frac1poly(n), incurs only a constant error and requires only a constant number of messages per party. Previously, such a protocol was known only for Omega(log n) messages per party.
Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Sep 2019.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 6; 2022: 4; 2023: 1; 2024: 3; 2025: 1.
15 papers.
Sort:
- 2021The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregationcited by 127
- 2022The Fundamental Price of Secure Aggregation in Differentially Private Federated Learningcited by 39
- 2022Stronger Privacy Amplification by Shuffling for R'enyi and Approximate Differential Privacycited by 29
- 2021Locally Private k-Means in One Roundcited by 19
- 2022Distributed Differential Privacy in Multi-Armed Banditscited by 14
- 2021Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregationcited by 10
- 2021User-Level Private Learning via Correlated Samplingcited by 9
- 2023Samplable Anonymous Aggregation for Private Federated Data Analysiscited by 6
- 2022Differential Secrecy for Distributed Data and Applications to Robust Differentially Secure Vector Summationcited by 5
- 2024Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messagescited by 5
- 2024FastLloyd: Federated, Accurate, Secure, and Tunable k-Means Clustering with Differential Privacycited by 2
- 2021The Sample Complexity of Distribution-Free Parity Learning in the Robust Shuffle Modelcited by 1
3 more
- 2025PREAMBLE: Private and Efficient Aggregation via Block Sparse Vectorscited by 1
- 2021Infinitely Divisible Noise in the Low Privacy Regimecited by 0
- 2024Differential Privacy on Trust Graphscited by 0
- 2025PREAMBLE: Private and Efficient Aggregation via Block Sparse Vectors14 Mar 2025
- 2024Differential Privacy on Trust Graphs15 Oct 2024
- 2024FastLloyd: Federated, Accurate, Secure, and Tunable k-Means Clustering with Differential Privacy3 May 2024
- 2024Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages16 Apr 2024
- 2023Samplable Anonymous Aggregation for Private Federated Data Analysis27 Jul 2023
- 2022Stronger Privacy Amplification by Shuffling for R'enyi and Approximate Differential Privacy9 Aug 2022
- 2022Distributed Differential Privacy in Multi-Armed Bandits12 Jun 2022
- 2022The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning7 Mar 2022
- 2022Differential Secrecy for Distributed Data and Applications to Robust Differentially Secure Vector Summation22 Feb 2022
- 2021Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregation3 Nov 2021
- 2021User-Level Private Learning via Correlated Sampling21 Oct 2021
- 2021Infinitely Divisible Noise in the Low Privacy Regime13 Oct 2021
- 2021The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregationcited by 127
- 2022The Fundamental Price of Secure Aggregation in Differentially Private Federated Learningcited by 39
- 2022Stronger Privacy Amplification by Shuffling for R'enyi and Approximate Differential Privacycited by 29
- 2021Locally Private k-Means in One Roundcited by 19
- 2022Distributed Differential Privacy in Multi-Armed Banditscited by 14
- 2021Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregationcited by 10
- 2021User-Level Private Learning via Correlated Samplingcited by 9
- 2023Samplable Anonymous Aggregation for Private Federated Data Analysiscited by 6
- 2022Differential Secrecy for Distributed Data and Applications to Robust Differentially Secure Vector Summationcited by 5
- 2024Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messagescited by 5
- 2024FastLloyd: Federated, Accurate, Secure, and Tunable k-Means Clustering with Differential Privacycited by 2
- 2021The Sample Complexity of Distribution-Free Parity Learning in the Robust Shuffle Modelcited by 1
12 most cited of 15 All 15 (JSON)
- 2025PREAMBLE: Private and Efficient Aggregation via Block Sparse Vectors14 Mar 2025
- 2024Differential Privacy on Trust Graphs15 Oct 2024
- 2024FastLloyd: Federated, Accurate, Secure, and Tunable k-Means Clustering with Differential Privacy3 May 2024
- 2024Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages16 Apr 2024
- 2023Samplable Anonymous Aggregation for Private Federated Data Analysis27 Jul 2023
- 2022Stronger Privacy Amplification by Shuffling for R'enyi and Approximate Differential Privacy9 Aug 2022
- 2022Distributed Differential Privacy in Multi-Armed Bandits12 Jun 2022
- 2022The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning7 Mar 2022
- 2022Differential Secrecy for Distributed Data and Applications to Robust Differentially Secure Vector Summation22 Feb 2022
- 2021Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregation3 Nov 2021
- 2021User-Level Private Learning via Correlated Sampling21 Oct 2021
- 2021Infinitely Divisible Noise in the Low Privacy Regime13 Oct 2021
12 newest of 15 All 15 (JSON)