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08/21/2023
Unlocking Accuracy and Fairness in Differentially Private Image Classification
Privacy-preserving machine learning aims to train models on private data...
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05/18/2023
Amplification by Shuffling without Shuffling
Motivated by recent developments in the shuffle model of differential pr...
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02/27/2023
Differentially Private Diffusion Models Generate Useful Synthetic Images
The ability to generate privacy-preserving synthetic versions of sensiti...
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02/15/2023
Tight Auditing of Differentially Private Machine Learning
Auditing mechanisms for differential privacy use probabilistic means to ...
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01/30/2023
Extracting Training Data from Diffusion Models
Image diffusion models such as DALL-E 2, Imagen, and Stable Diffusion ha...
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04/28/2022
Unlocking High-Accuracy Differentially Private Image Classification through Scale
Differential Privacy (DP) provides a formal privacy guarantee preventing...
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01/13/2022
Reconstructing Training Data with Informed Adversaries
Given access to a machine learning model, can an adversary reconstruct t...
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01/06/2022
Learning to be adversarially robust and differentially private
We study the difficulties in learning that arise from robust and differe...
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12/08/2021
Ethical and social risks of harm from Language Models
This paper aims to help structure the risk landscape associated with lar...
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02/16/2021
A Law of Robustness for Weight-bounded Neural Networks
Robustness of deep neural networks against adversarial perturbations is ...
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09/18/2020
Private Reinforcement Learning with PAC and Regret Guarantees
Motivated by high-stakes decision-making domains like personalized medic...
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07/13/2020
Privacy Amplification via Random Check-Ins
Differentially Private Stochastic Gradient Descent (DP-SGD) forms a fund...
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02/03/2020
Private Summation in the Multi-Message Shuffle Model
The shuffle model of differential privacy (Erlingsson et al. SODA 2019; ...
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10/20/2019
Privacy- and Utility-Preserving Textual Analysis via Calibrated Multivariate Perturbations
Accurately learning from user data while providing quantifiable privacy ...
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10/14/2019
Actor Critic with Differentially Private Critic
Reinforcement learning algorithms are known to be sample inefficient, an...
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09/24/2019
Improved Summation from Shuffling
A protocol by Ishai et al. (FOCS 2006) showing how to implement distribu...
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06/20/2019
Differentially Private Summation with Multi-Message Shuffling
In recent work, Cheu et al. (Eurocrypt 2019) proposed a protocol for n-p...
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05/29/2019
Privacy Amplification by Mixing and Diffusion Mechanisms
A fundamental result in differential privacy states that the privacy gua...
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05/26/2019
Automatic Discovery of Privacy-Utility Pareto Fronts
Differential privacy is a mathematical framework for privacy-preserving ...
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05/24/2019
Hypothesis Testing Interpretations and Renyi Differential Privacy
Differential privacy is the gold standard in data privacy, with applicat...
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03/26/2019
Privacy-preserving Active Learning on Sensitive Data for User Intent Classification
Active learning holds promise of significantly reducing data annotation ...
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03/07/2019
The Privacy Blanket of the Shuffle Model
This work studies differential privacy in the context of the recently pr...
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10/17/2018
Hierarchical Methods of Moments
Spectral methods of moments provide a powerful tool for learning the par...
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07/31/2018
Subsampled Rényi Differential Privacy and Analytical Moments Accountant
We study the problem of subsampling in differential privacy (DP), a ques...
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07/04/2018
Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences
Differential privacy comes equipped with multiple analytical tools for t...
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05/16/2018
Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising
The Gaussian mechanism is an essential building block used in multitude ...
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03/07/2016
Differentially Private Policy Evaluation
We present the first differentially private algorithms for reinforcement...
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06/27/2012