Differential Privacy

February 9, 2023

On the Privacy-Robustness-Utility Trilemma in Distributed Learning
Youssef Allouah, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan
Native Robustness Differential Privacy Adversarial Learning Distributed Learning Utility Privacy Trade Robust Aggregation Rule

February 8, 2023

Exploratory Analysis of Federated Learning Methods with Differential Privacy on MIMIC-III
Aron N. Horvath, Matteo Berchier, Farhad Nooralahzadeh, Ahmed Allam, Michael Krauthammer
Differential Privacy Federated Learning Exploratory Study Medical Information Mart for Intensive
DIFF2: Differential Private Optimization via Gradient Differences for Nonconvex Distributed Learning
Tomoya Murata, Taiji Suzuki
LeArning Abstract Differential Privacy Private Stochastic General Nonconvex Private Gradient Gradient Difference

February 6, 2023

February 3, 2023

Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging
Soroosh Tayebi Arasteh, Alexander Ziller, Christiane Kuhl, Marcus Makowski, Sven Nebelung, Rickmer Braren, Daniel Rueckert, Daniel Truhn, Georgios Kaissis
Differential Privacy Medical Imaging Privacy Preserving Large Scale Training Privacy Preserving Training Fairness Estimation

February 2, 2023

February 1, 2023

Analyzing Leakage of Personally Identifiable Information in Language Models
Nils Lukas, Ahmed Salem, Robert Sim, Shruti Tople, Lukas Wutschitz, Santiago Zanella-Béguelin
Language Model Differential Privacy Personal Information Defense Algorithm Leakage Attack Life Event

January 31, 2023

General Gaussian Noise Mechanisms and Their Optimality for Unbiased Mean Estimation
Aleksandar Nikolov, Haohua Tang
Differential Privacy High Dimensional Near Optimality Local Differential Privacy Gaussian Mechanism Private Mean Estimation Unbiased Estimator

January 30, 2023

Equivariant Differentially Private Deep Learning: Why DP-SGD Needs Sparser Models
Florian A. Hölzl, Daniel Rueckert, Georgios Kaissis
Differential Privacy Private Stochastic Gradient Descent Equivariant Map Equivariant Convolutional Neural Network Sparse Local Model

January 29, 2023

Concurrent Shuffle Differential Privacy Under Continual Observation
Jay Tenenbaum, Haim Kaplan, Yishay Mansour, Uri Stemmer
Differential Privacy Shuffle Model

January 28, 2023

January 27, 2023

FedPH: Privacy-enhanced Heterogeneous Federated Learning
Kuang Hangdong, Mi Bo
Differential Privacy Federated Learning Privacy Preserving Heterogeneous Federated Learning

January 24, 2023

Membership Inference of Diffusion Models
Hailong Hu, Jun Pang
Diffusion Model Differential Privacy Membership Inference Attack Membership Inference Privacy Sensitive Likelihood Ratio Attack

January 22, 2023

Differentially Private Natural Language Models: Recent Advances and Future Directions
Lijie Hu, Ivan Habernal, Lei Shen, Di Wang
Language Model Natural Language Processing Differential Privacy Future Direction Recent Advance Private Data Analysis

January 21, 2023

Statistical Theory of Differentially Private Marginal-based Data Synthesis Algorithms
Ximing Li, Chendi Wang, Guang Cheng
Differential Privacy Probabilistic Graphical Model Statistical Model Marginal Distribution

January 20, 2023

Differential Privacy in Cooperative Multiagent Planning
Bo Chen, Calvin Hawkins, Mustafa O. Karabag, Cyrus Neary, Matthew Hale, Ufuk Topcu
Differential Privacy Markov Game Agent Communication Multi Agent Planning Differential Privacy Mechanism Competitive Agent

January 16, 2023

Enforcing Privacy in Distributed Learning with Performance Guarantees
Elsa Rizk, Stefan Vlaski, Ali H. Sayed
Differential Privacy Optimization Purpose Privacy Preservation Distributed Learning Performance Guarantee Calysto Scheme Weight Perturbation Efficient Homomorphic

Papers

On the Privacy-Robustness-Utility Trilemma in Distributed Learning

Exploratory Analysis of Federated Learning Methods with Differential Privacy on MIMIC-III

DIFF2: Differential Private Optimization via Gradient Differences for Nonconvex Distributed Learning

An Empirical Analysis of Fairness Notions under Differential Privacy

On Private and Robust Bandits

Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging

Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy

FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations

Analyzing Leakage of Personally Identifiable Information in Language Models

General Gaussian Noise Mechanisms and Their Optimality for Unbiased Mean Estimation

Equivariant Differentially Private Deep Learning: Why DP-SGD Needs Sparser Models

Concurrent Shuffle Differential Privacy Under Continual Observation

Context-Aware Differential Privacy for Language Modeling

(Private) Kernelized Bandits with Distributed Biased Feedback

FedPH: Privacy-enhanced Heterogeneous Federated Learning

Membership Inference of Diffusion Models

Differentially Private Natural Language Models: Recent Advances and Future Directions

Statistical Theory of Differentially Private Marginal-based Data Synthesis Algorithms

Differential Privacy in Cooperative Multiagent Planning

Enforcing Privacy in Distributed Learning with Performance Guarantees