1.6k citations · 2.2k across the 52 of their papers we have counts for
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Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy
Daphne Ippolito, Florian Tramèr, Milad Nasr +5
Studying data memorization in neural language models helps us understand the risks (e.g., to privacy or copyright) associated with models regurgitating training data and aids in th…
No Free Lunch in "Privacy for Free: How does Dataset Condensation Help Privacy"
Nicholas Carlini, Vitaly Feldman, Milad Nasr
New methods designed to preserve data privacy require careful scrutiny. Failure to preserve privacy is hard to detect, and yet can lead to catastrophic results when a system implem…
FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learning
Meisam Hejazinia, Dzmitry Huba, Ilias Leontiadis +7
Federated learning (FL) has emerged as an effective approach to address consumer privacy needs. FL has been successfully applied to certain machine learning tasks, such as training…