311 citations · 675 across the 6 of their papers we have counts for
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cs.LG2018★ 3 cited
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…
stat.ML2018★ 311 cited
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…