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How to dp-fy ml: A practical guide to machine learning with differential privacy
Abstract Machine Learning (ML) models are ubiquitous in real-world applications and are a
constant focus of research. Modern ML models have become more complex, deeper, and …
constant focus of research. Modern ML models have become more complex, deeper, and …
Differentially private natural language models: Recent advances and future directions
Recent developments in deep learning have led to great success in various natural
language processing (NLP) tasks. However, these applications may involve data that …
language processing (NLP) tasks. However, these applications may involve data that …
Federated learning of gboard language models with differential privacy
We train language models (LMs) with federated learning (FL) and differential privacy (DP) in
the Google Keyboard (Gboard). We apply the DP-Follow-the-Regularized-Leader (DP …
the Google Keyboard (Gboard). We apply the DP-Follow-the-Regularized-Leader (DP …
Privacy side channels in machine learning systems
Most current approaches for protecting privacy in machine learning (ML) assume that
models exist in a vacuum. Yet, in reality, these models are part of larger systems that include …
models exist in a vacuum. Yet, in reality, these models are part of larger systems that include …
(Amplified) Banded Matrix Factorization: A unified approach to private training
Matrix factorization (MF) mechanisms for differential privacy (DP) have substantially
improved the state-of-the-art in privacy-utility-computation tradeoffs for ML applications in a …
improved the state-of-the-art in privacy-utility-computation tradeoffs for ML applications in a …
On the convergence of federated averaging with cyclic client participation
Abstract Federated Averaging (FedAvg) and its variants are the most popular optimization
algorithms in federated learning (FL). Previous convergence analyses of FedAvg either …
algorithms in federated learning (FL). Previous convergence analyses of FedAvg either …
Constant matters: Fine-grained error bound on differentially private continual observation
We study fine-grained error bounds for differentially private algorithms for counting under
continual observation. Our main insight is that the matrix mechanism when using lower …
continual observation. Our main insight is that the matrix mechanism when using lower …
Fine-tuning large language models with user-level differential privacy
We investigate practical and scalable algorithms for training large language models (LLMs)
with user-level differential privacy (DP) in order to provably safeguard all the examples …
with user-level differential privacy (DP) in order to provably safeguard all the examples …
One-shot empirical privacy estimation for federated learning
Privacy estimation techniques for differentially private (DP) algorithms are useful for
comparing against analytical bounds, or to empirically measure privacy loss in settings …
comparing against analytical bounds, or to empirically measure privacy loss in settings …
Efficient and near-optimal noise generation for streaming differential privacy
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 revealing …
and our goal is to output an approximate running total of these increments, without revealing …