Differential Privacy: What is all the noise about?
Authors: Roxana Danger | Published: 2022-05-19
DP-SGD
What You See is What You Get: Principled Deep Learning via Distributional Generalization
Authors: Bogdan Kulynych, Yao-Yuan Yang, Yaodong Yu, Jarosław Błasiok, Preetum Nakkiran | Published: 2022-04-07 | Updated: 2022-10-17
Transfer Learning In Differential Privacy’s Hybrid-Model
Authors: Refael Kohen, Or Sheffet | Published: 2022-01-28 | Updated: 2022-06-16
Improving Differentially Private SGD via Randomly Sparsified Gradients
Authors: Junyi Zhu, Matthew B. Blaschko | Published: 2021-12-01 | Updated: 2023-06-28
Evaluating Privacy-Preserving Machine Learning in Critical Infrastructures: A Case Study on Time-Series Classification
Authors: Dominique Mercier, Adriano Lucieri, Mohsin Munir, Andreas Dengel, Sheraz Ahmed | Published: 2021-11-29
Node-Level Differentially Private Graph Neural Networks
Authors: Ameya Daigavane, Gagan Madan, Aditya Sinha, Abhradeep Guha Thakurta, Gaurav Aggarwal, Prateek Jain | Published: 2021-11-23 | Updated: 2022-08-26
The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection
Authors: Shubhankar Mohapatra, Sajin Sasy, Xi He, Gautam Kamath, Om Thakkar | Published: 2021-11-09
Not all noise is accounted equally: How differentially private learning benefits from large sampling rates
Authors: Friedrich Dörmann, Osvald Frisk, Lars Nørvang Andersen, Christian Fischer Pedersen | Published: 2021-10-12
Generalization Techniques Empirically Outperform Differential Privacy against Membership Inference
Authors: Jiaxiang Liu, Simon Oya, Florian Kerschbaum | Published: 2021-10-11
Combining Differential Privacy and Byzantine Resilience in Distributed SGD
Authors: Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, Sebastien Rouault, John Stephan | Published: 2021-10-08 | Updated: 2023-10-05