6.3k citations · 8.5k across the 15 of their papers we have counts for
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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…
Scalable Private Learning with PATE
Nicolas Papernot, Shuang Song, Ilya Mironov +3
The rapid adoption of machine learning has increased concerns about the privacy implications of machine learning models trained on sensitive data, such as medical records or other…