research
∙
05/28/2023
Training Private Models That Know What They Don't Know
Training reliable deep learning models which avoid making overconfident ...
share
research
∙
05/22/2023
Faster Differentially Private Convex Optimization via Second-Order Methods
Differentially private (stochastic) gradient descent is the workhorse of...
research
∙
03/31/2023
Differentially Private Stream Processing at Scale
We design, to the best of our knowledge, the first differentially privat...
research
∙
03/01/2023
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy
ML models are ubiquitous in real world applications and are a constant f...
research
∙
02/20/2023
Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks
We consider the problem of minimizing a non-convex objective while prese...
research
∙
02/19/2023
Why Is Public Pretraining Necessary for Private Model Training?
In the privacy-utility tradeoff of a model trained on benchmark language...
research
∙
11/24/2022
Differentially Private Image Classification from Features
Leveraging transfer learning has recently been shown to be an effective ...
research
∙
11/12/2022
Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning
We introduce new differentially private (DP) mechanisms for gradient-bas...
research
∙
10/05/2022
Fine-Tuning with Differential Privacy Necessitates an Additional Hyperparameter Search
Models need to be trained with privacy-preserving learning algorithms to...
research
∙
10/04/2022
Recycling Scraps: Improving Private Learning by Leveraging Intermediate Checkpoints
All state-of-the-art (SOTA) differentially private machine learning (DP ...
research
∙
07/11/2022
(Nearly) Optimal Private Linear Regression via Adaptive Clipping
We study the problem of differentially private linear regression where e...
research
∙
05/06/2022
Large Scale Transfer Learning for Differentially Private Image Classification
Differential Privacy (DP) provides a formal framework for training machi...
research
∙
04/04/2022
Langevin Diffusion: An Almost Universal Algorithm for Private Euclidean (Convex) Optimization
In this paper we revisit the problem of differentially private empirical...
research
∙
08/14/2020
Dimension Independence in Unconstrained Private ERM via Adaptive Preconditioning
In this paper we revisit the problem of private empirical risk minimziat...
research
∙
07/13/2020
Privacy Amplification via Random Check-Ins
Differentially Private Stochastic Gradient Descent (DP-SGD) forms a fund...
research
∙
03/26/2020
Obliviousness Makes Poisoning Adversaries Weaker
Poisoning attacks have emerged as a significant security threat to machi...
research
∙
02/10/2020
Guidelines for Implementing and Auditing Differentially Private Systems
Differential privacy is an information theoretic constraint on algorithm...
research
∙
08/27/2019
Private Stochastic Convex Optimization with Optimal Rates
We study differentially private (DP) algorithms for stochastic convex op...
research
∙
11/29/2018
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity
Sensitive statistics are often collected across sets of users, with repe...
research
∙
08/20/2018
Privacy Amplification by Iteration
Many commonly used learning algorithms work by iteratively updating an i...
research
∙
03/14/2018
Model-Agnostic Private Learning via Stability
We design differentially private learning algorithms that are agnostic t...
research
∙
12/28/2017
Differentially Private Matrix Completion, Revisited
We study the problem of privacy-preserving collaborative filtering where...
research
∙
03/06/2015
To Drop or Not to Drop: Robustness, Consistency and Differential Privacy Properties of Dropout
Training deep belief networks (DBNs) requires optimizing a non-convex fu...
research
∙
11/20/2014
Private Empirical Risk Minimization Beyond the Worst Case: The Effect of the Constraint Set Geometry
Empirical Risk Minimization (ERM) is a standard technique in machine lea...
research
∙
05/27/2014
Differentially Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds
In this paper, we initiate a systematic investigation of differentially ...
research
∙
09/01/2011
