Stronger Privacy Amplification by Shuffling for R\'enyi and Approximate Differential Privacy. (arXiv:2208.04591v1 [cs.CR])

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 …

amplification differential privacy privacy