74 citations · 81 across the 10 of their papers we have counts for
4 papers · 1 filter
Insufficient Statistics Perturbation: Stable Estimators for Private Least Squares
Gavin Brown, Jonathan Hayase, Samuel Hopkins +5
We present a sample- and time-efficient differentially private algorithm for ordinary least squares, with error that depends linearly on the dimension and is independent of the con…
Characterizing the Training Dynamics of Private Fine-tuning with Langevin diffusion
Shuqi Ke, Charlie Hou, Sewoong Oh +1
We show that differentially private full fine-tuning (DP-FFT) can distort pre-trained backbone features based on both theoretical and empirical results. We identify the cause of th…
Label Poisoning is All You Need
Rishi D. Jha, Jonathan Hayase, Sewoong Oh
In a backdoor attack, an adversary injects corrupted data into a model's training dataset in order to gain control over its predictions on images with a specific attacker-defined t…
Towards a Defense Against Federated Backdoor Attacks Under Continuous Training
Shuaiqi Wang, Jonathan Hayase, Giulia Fanti +1
Backdoor attacks are dangerous and difficult to prevent in federated learning (FL), where training data is sourced from untrusted clients over long periods of time. These difficult…