142 citations · 842 across the 49 of their papers we have counts for
8 papers · 1 filter
Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks
Jimmy Z. Di, Jack Douglas, Jayadev Acharya +2
We introduce camouflaged data poisoning attacks, a new attack vector that arises in the context of machine unlearning and other settings when model retraining may be induced. An ad…
Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining
Florian Tramèr, Gautam Kamath, Nicholas Carlini
The performance of differentially private machine learning can be boosted significantly by leveraging the transfer learning capabilities of non-private models pretrained on large p…
Robustness Implies Privacy in Statistical Estimation
Samuel B. Hopkins, Gautam Kamath, Mahbod Majid +1
We study the relationship between adversarial robustness and differential privacy in high-dimensional algorithmic statistics. We give the first black-box reduction from privacy to…
Private Estimation with Public Data
Alex Bie, Gautam Kamath, Vikrant Singhal
We initiate the study of differentially private (DP) estimation with access to a small amount of public data. For private estimation of d-dimensional Gaussians, we assume that the…
Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent
Da Yu, Gautam Kamath, Janardhan Kulkarni +3
Differentially private stochastic gradient descent (DP-SGD) is the workhorse algorithm for recent advances in private deep learning. It provides a single privacy guarantee to all d…
New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma
Gautam Kamath, Argyris Mouzakis, Vikrant Singhal
We prove new lower bounds for statistical estimation tasks under the constraint of -differential privacy. First, we provide tight lower bounds for private covaria…
Indiscriminate Data Poisoning Attacks on Neural Networks
Yiwei Lu, Gautam Kamath, Yaoliang Yu
Data poisoning attacks, in which a malicious adversary aims to influence a model by injecting "poisoned" data into the training process, have attracted significant recent attention…
Calibration with Privacy in Peer Review
Wenxin Ding, Gautam Kamath, Weina Wang +1
Reviewers in peer review are often miscalibrated: they may be strict, lenient, extreme, moderate, etc. A number of algorithms have previously been proposed to calibrate reviews. Su…