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09/11/2023
Privacy Side Channels in Machine Learning Systems
Most current approaches for protecting privacy in machine learning (ML) ...
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09/09/2023
MADLAD-400: A Multilingual And Document-Level Large Audited Dataset
We introduce MADLAD-400, a manually audited, general domain 3T token mon...
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07/20/2023
Private Federated Learning with Autotuned Compression
We propose new techniques for reducing communication in private federate...
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06/26/2023
Are aligned neural networks adversarially aligned?
Large language models are now tuned to align with the goals of their cre...
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06/13/2023
(Amplified) Banded Matrix Factorization: A unified approach to private training
Matrix factorization (MF) mechanisms for differential privacy (DP) have ...
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05/29/2023
Federated Learning of Gboard Language Models with Differential Privacy
We train language models (LMs) with federated learning (FL) and differen...
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02/20/2023
Poisoning Web-Scale Training Datasets is Practical
Deep learning models are often trained on distributed, webscale datasets...
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11/23/2022
Private Multi-Winner Voting for Machine Learning
Private multi-winner voting is the task of revealing k-hot binary vector...
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11/12/2022
Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning
We introduce new differentially private (DP) mechanisms for gradient-bas...
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10/31/2022
Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy
Studying data memorization in neural language models helps us understand...
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10/05/2022
Fine-Tuning with Differential Privacy Necessitates an Additional Hyperparameter Search
Models need to be trained with privacy-preserving learning algorithms to...
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08/06/2022
On the Fundamental Limits of Formally (Dis)Proving Robustness in Proof-of-Learning
Proof-of-learning (PoL) proposes a model owner use machine learning trai...
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03/07/2022
The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning
We consider the problem of training a d dimensional model with distribut...
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03/09/2021
Proof-of-Learning: Definitions and Practice
Training machine learning (ML) models typically involves expensive itera...
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02/09/2021
CaPC Learning: Confidential and Private Collaborative Learning
Machine learning benefits from large training datasets, which may not al...
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07/28/2020
Label-Only Membership Inference Attacks
Membership inference attacks are one of the simplest forms of privacy le...
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02/27/2020
Entangled Watermarks as a Defense against Model Extraction
Machine learning involves expensive data collection and training procedu...
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10/13/2019
