157 citations · 741 across the 64 of their papers we have counts for
3 papers · 1 filter
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity
Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov +3
Sensitive statistics are often collected across sets of users, with repeated collection of reports done over time. For example, trends in users' private preferences or software usa…
Privacy Amplification by Iteration
Vitaly Feldman, Ilya Mironov, Kunal Talwar +1
Many commonly used learning algorithms work by iteratively updating an intermediate solution using one or a few data points in each iteration. Analysis of differential privacy for…
Model-Agnostic Private Learning via Stability
Raef Bassily, Om Thakkar, Abhradeep Thakurta
We design differentially private learning algorithms that are agnostic to the learning model. Our algorithms are interactive in nature, i.e., instead of outputting a model based on…