Privacy and fairness in Federated learning: on the perspective of Tradeoff
Federated learning (FL) has been a hot topic in recent years. Ever since it was introduced,
researchers have endeavored to devise FL systems that protect privacy or ensure fair …
researchers have endeavored to devise FL systems that protect privacy or ensure fair …
Optimal differentially private learning with public data
Differential Privacy (DP) ensures that training a machine learning model does not leak
private data. However, the cost of DP is lower model accuracy or higher sample complexity …
private data. However, the cost of DP is lower model accuracy or higher sample complexity …
Holistic Survey of Privacy and Fairness in Machine Learning
Privacy and fairness are two crucial pillars of responsible Artificial Intelligence (AI) and
trustworthy Machine Learning (ML). Each objective has been independently studied in the …
trustworthy Machine Learning (ML). Each objective has been independently studied in the …
Individual privacy accounting for differentially private stochastic gradient descent
Differentially private stochastic gradient descent (DP-SGD) is the workhorse algorithm for
recent advances in private deep learning. It provides a single privacy guarantee to all …
recent advances in private deep learning. It provides a single privacy guarantee to all …
How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization
We provide a simple and flexible framework for designing differentially private algorithms to
find approximate stationary points of non-convex loss functions. Our framework is based on …
find approximate stationary points of non-convex loss functions. Our framework is based on …
Mirror Descent Algorithms with Nearly Dimension-Independent Rates for Differentially-Private Stochastic Saddle-Point Problems
We study the problem of differentially-private (DP) stochastic (convex-concave) saddle-
points in the polyhedral setting. We propose $(\varepsilon,\delta) $-DP algorithms based on …
points in the polyhedral setting. We propose $(\varepsilon,\delta) $-DP algorithms based on …
Differentially Private Fair Binary Classifications
H Ghoukasian, S Asoodeh - arXiv preprint arXiv:2402.15603, 2024 - arxiv.org
In this work, we investigate binary classification under the constraints of both differential
privacy and fairness. We first propose an algorithm based on the decoupling technique for …
privacy and fairness. We first propose an algorithm based on the decoupling technique for …
Toward the Tradeoffs Between Privacy, Fairness and Utility in Federated Learning
Federated Learning (FL) is a novel privacy-protection distributed machine learning
paradigm that guarantees user privacy and prevents the risk of data leakage due to the …
paradigm that guarantees user privacy and prevents the risk of data leakage due to the …
Differentially Private and Fair Optimization for Machine Learning: Tight Error Bounds and Efficient Algorithms
A Lowy - 2023 - search.proquest.com
In recent years, machine learning (ML) systems have increasingly been deployed in
industry, government, and society. Although ML models can be extremely useful and …
industry, government, and society. Although ML models can be extremely useful and …