Improved Summation from Shuffling
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Abstract (arXiv)
A protocol by Ishai et al. (FOCS 2006) showing how to implement distributed n-party summation from secure shuffling has regained relevance in the context of the recently proposed shuffle model of differential privacy, as it allows to attain the accuracy levels of the curator model at a moderate communication cost. To achieve statistical security 2-σ, the protocol by Ishai et al. requires the number of messages sent by each party to em grow logarithmically with n as O(log n + σ). In this note we give an improved analysis achieving a dependency of the form O(1+σ/log n). Conceptually, this addresses the intuitive question left open by Ishai et al. of whether the shuffling step in their protocol provides a "hiding in the crowd" amplification effect as n increases. From a practical perspective, our analysis provides explicit constants and shows, for example, that the method of Ishai et al. applied to summation of 32-bit numbers from n=104 parties sending 12 messages each provides statistical security 2-40.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Sep 2019.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 0; 2024: 0; 2025: 2.
4 papers.
- 2021On the Renyi Differential Privacy of the Shuffle Modelcited by 16
- 2021Renyi Differential Privacy of the Subsampled Shuffle Model in Distributed Learningcited by 6
- 2025Learning from End User Data with Shuffled Differential Privacy over Kernel Densitiescited by 1
- 2025How to Securely Shuffle? A survey about Secure Shufflers for privacy-preserving computationscited by 1
- 2025How to Securely Shuffle? A survey about Secure Shufflers for privacy-preserving computations2 Jul 2025
- 2025Learning from End User Data with Shuffled Differential Privacy over Kernel Densities19 Feb 2025
- 2021Renyi Differential Privacy of the Subsampled Shuffle Model in Distributed Learning19 Jul 2021
- 2021On the Renyi Differential Privacy of the Shuffle Model11 May 2021