238 citations · 805 across the 38 of their papers we have counts for
5 papers · 1 filter
Private Multi-Winner Voting for Machine Learning
Adam Dziedzic, Christopher A Choquette-Choo, Natalie Dullerud +6
Private multi-winner voting is the task of revealing -hot binary vectors satisfying a bounded differential privacy (DP) guarantee. This task has been understudied in machine lea…
Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning
Christopher A. Choquette-Choo, H. Brendan McMahan, Keith Rush +1
We introduce new differentially private (DP) mechanisms for gradient-based machine learning (ML) with multiple passes (epochs) over a dataset, substantially improving the achievabl…
Fine-Tuning with Differential Privacy Necessitates an Additional Hyperparameter Search
Yannis Cattan, Christopher A. Choquette-Choo, Nicolas Papernot +1
Models need to be trained with privacy-preserving learning algorithms to prevent leakage of possibly sensitive information contained in their training data. However, canonical algo…
Proof-of-Learning is Currently More Broken Than You Think
Congyu Fang, Hengrui Jia, Anvith Thudi +5
Proof-of-Learning (PoL) proposes that a model owner logs training checkpoints to establish a proof of having expended the computation necessary for training. The authors of PoL for…
The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning
Wei-Ning Chen, Christopher A. Choquette-Choo, Peter Kairouz +1
We consider the problem of training a dimensional model with distributed differential privacy (DP) where secure aggregation (SecAgg) is used to ensure that the server only sees…