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06/14/2023
Augment then Smooth: Reconciling Differential Privacy with Certified Robustness
Machine learning models are susceptible to a variety of attacks that can...
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05/24/2023
Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models
Large language models (LLMs) are excellent in-context learners. However,...
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03/29/2023
Have it your way: Individualized Privacy Assignment for DP-SGD
When training a machine learning model with differential privacy, one se...
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02/17/2023
Learning with Impartiality to Walk on the Pareto Frontier of Fairness, Privacy, and Utility
Deploying machine learning (ML) models often requires both fairness and ...
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01/09/2023
Is Federated Learning a Practical PET Yet?
Federated learning (FL) is a framework for users to jointly train a mach...
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01/09/2023
Introducing Model Inversion Attacks on Automatic Speaker Recognition
Model inversion (MI) attacks allow to reconstruct average per-class repr...
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11/18/2022
A Unified Framework for Quantifying Privacy Risk in Synthetic Data
Synthetic data is often presented as a method for sharing sensitive info...
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09/16/2022
Dataset Inference for Self-Supervised Models
Self-supervised models are increasingly prevalent in machine learning (M...
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02/24/2022
Bounding Membership Inference
Differential Privacy (DP) is the de facto standard for reasoning about t...
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02/21/2022
Personalized PATE: Differential Privacy for Machine Learning with Individual Privacy Guarantees
Applying machine learning (ML) to sensitive domains requires privacy pro...
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12/06/2021
When the Curious Abandon Honesty: Federated Learning Is Not Private
In federated learning (FL), data does not leave personal devices when th...
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05/17/2021
Gradient Masking and the Underestimated Robustness Threats of Differential Privacy in Deep Learning
An important problem in deep learning is the privacy and security of neu...
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09/25/2020
A Survey on Model Watermarking Neural Networks
Machine learning (ML) models are applied in an increasing variety of dom...
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02/09/2018
