36 citations · 81 across the 4 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…
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…
The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks
Nicholas Carlini, Chang Liu, Úlfar Erlingsson +2
This paper describes a testing methodology for quantitatively assessing the risk that rare or unique training-data sequences are unintentionally memorized by generative sequence mo…