# Stronger Privacy Amplification by Shuffling for R\\'enyi and Approximate Differential Privacy

Source: [Stronger Privacy Amplification by Shuffling for R\\'enyi and Approximate Differential Privacy](<https://www.sota2.com/research/paper/68fa71061e209131cea4eabc>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:55:42.510Z

Authors: Vitaly Feldman, Audra McMillan, Kunal Talwar

Published: 2022-08-09T08:13:48.000Z

## Abstract

The shuffle model of differential privacy has gained significant interest as an intermediate trust model between the standard local and central models \[EFMRTT19; CSUZZ19\]. A key result in this model is that randomly shuffling locally randomized data amplifies differential privacy guarantees. Such amplification implies substantially stronger privacy guarantees for systems in which data is contributed anonymously \[BEMMRLRKTS17\].   In this work, we improve the state of the art privacy amplification by shuffling results both theoretically and numerically. Our first contribution is the first asymptotically optimal analysis of the R\\'enyi differential privacy parameters for the shuffled outputs of LDP randomizers. Our second contribution is a new analysis of privacy amplification by shuffling. This analysis improves on the techniques of \[FMT20\] and leads to tighter numerical bounds in all parameter settings.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Privacy Budget Estimation on Shuffled Mechanisms n=10000, ε0=4.444](<https://www.sota2.com/research/sota/privacy-budget-estimation-on-shuffled-mechanisms-n-10000-0-4-444.md>): Privacy budget estimation; Shuffled Mechanisms n=10000, ε0=4.444; Privacy Budget (Epsilon): 0.369.

- [Privacy Budget Estimation on Shuffled mechanism (n=10000, epsilon\_0=4.444) (test)](<https://www.sota2.com/research/sota/privacy-budget-estimation-on-shuffled-mechanism-n-10000-epsilon_0-4-444-test.md>): Privacy budget estimation; Shuffled mechanism (n=10000, epsilon\_0=4.444) (test); Delta Score: 0.9494.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2208.04591>)

[PDF](<https://arxiv.org/pdf/2208.04591.pdf>)