157 citations · 741 across the 64 of their papers we have counts for
8 papers · 1 filter
Preserving Expert-Level Privacy in Offline Reinforcement Learning
Navodita Sharma, Vishnu Vinod, Abhradeep Thakurta +4
The offline reinforcement learning (RL) problem aims to learn an optimal policy from historical data collected by one or more behavioural policies (experts) by interacting with an…
Differentially Private Parameter-Efficient Fine-tuning for Large ASR Models
Hongbin Liu, Lun Wang, Om Thakkar +2
Large ASR models can inadvertently leak sensitive information, which can be mitigated by formal privacy measures like differential privacy (DP). However, traditional DP training is…
Near Exact Privacy Amplification for Matrix Mechanisms
Christopher A. Choquette-Choo, Arun Ganesh, Saminul Haque +2
We study the problem of computing the privacy parameters for DP machine learning when using privacy amplification via random batching and noise correlated across rounds via a corre…
The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD
Thomas Steinke, Milad Nasr, Arun Ganesh +7
We propose a simple heuristic privacy analysis of noisy clipped stochastic gradient descent (DP-SGD) in the setting where only the last iterate is released and the intermediate ite…
Training Large ASR Encoders with Differential Privacy
Geeticka Chauhan, Steve Chien, Om Thakkar +2
Self-supervised learning (SSL) methods for large speech models have proven to be highly effective at ASR. With the interest in public deployment of large pre-trained models, there…
Optimal Rates for -Smooth DP-SCO with a Single Epoch and Large Batches
Christopher A. Choquette-Choo, Arun Ganesh, Abhradeep Thakurta
In this paper we revisit the DP stochastic convex optimization (SCO) problem. For convex smooth losses, it is well-known that the canonical DP-SGD (stochastic gradient descent) ach…
Efficient and Near-Optimal Noise Generation for Streaming Differential Privacy
Krishnamurthy Dvijotham, H. Brendan McMahan, Krishna Pillutla +2
In the task of differentially private (DP) continual counting, we receive a stream of increments and our goal is to output an approximate running total of these increments, without…
Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation
Gavin Brown, Krishnamurthy Dvijotham, Georgina Evans +3
We provide an improved analysis of standard differentially private gradient descent for linear regression under the squared error loss. Under modest assumptions on the input, we ch…