How to dp-fy ml: A practical guide to machine learning with differential privacy

N Ponomareva, H Hazimeh, A Kurakin, Z Xu… - Journal of Artificial …, 2023 - jair.org
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 …

Anonymization: The imperfect science of using data while preserving privacy

A Gadotti, L Rocher, F Houssiau, AM Creţu… - Science …, 2024 - science.org
Information about us, our actions, and our preferences is created at scale through surveys or
scientific studies or as a result of our interaction with digital devices such as smartphones …

Advances and open problems in federated learning

P Kairouz, HB McMahan, B Avent… - … and trends® in …, 2021 - nowpublishers.com
Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …

Differentially private learning needs better features (or much more data)

F Tramer, D Boneh - ar**: Differentially private deep learning made easier and stronger
Z Bu, YX Wang, S Zha… - Advances in Neural …, 2024 - proceedings.neurips.cc
Per-example gradient clip** is a key algorithmic step that enables practical differential
private (DP) training for deep learning models. The choice of clip** threshold $ R …