A review of privacy-preserving techniques for deep learning
Deep learning is one of the advanced approaches of machine learning, and has attracted a
growing attention in the recent years. It is used nowadays in different domains and …
growing attention in the recent years. It is used nowadays in different domains and …
Achieving efficient and privacy-preserving neural network training and prediction in cloud environments
The neural network has been widely used to train predictive models for applications such as
image processing, disease prediction, and face recognition. To produce more accurate …
image processing, disease prediction, and face recognition. To produce more accurate …
Machine learning for security and the internet of things: the good, the bad, and the ugly
The advancement of the Internet of Things (IoT) has allowed for unprecedented data
collection, automation, and remote sensing and actuation, transforming autonomous …
collection, automation, and remote sensing and actuation, transforming autonomous …
Your labels are selling you out: Relation leaks in vertical federated learning
Vertical federated learning (VFL) is an emerging privacy-preserving paradigm that enables
collaboration between companies. These companies have the same set of users but …
collaboration between companies. These companies have the same set of users but …
Privacy-preserving deep learning for pervasive health monitoring: a study of environment requirements and existing solutions adequacy
In recent years, deep learning in healthcare applications has attracted considerable
attention from research community. They are deployed on powerful cloud infrastructures to …
attention from research community. They are deployed on powerful cloud infrastructures to …
GALA: Greedy computation for linear algebra in privacy-preserved neural networks
Machine Learning as a Service (MLaaS) is enabling a wide range of smart applications on
end devices. However, privacy-preserved computation is still expensive. Our investigation …
end devices. However, privacy-preserved computation is still expensive. Our investigation …
[PDF][PDF] Unifed: A benchmark for federated learning frameworks
Federated Learning (FL) has become a practical and popular paradigm in machine 1
learning. However, currently, there is no systematic solution that covers diverse 2 use cases …
learning. However, currently, there is no systematic solution that covers diverse 2 use cases …
Privacy-preserving federated adversarial domain adaptation over feature groups for interpretability
We present a novel privacy-preserving federated adversarial domain adaptation approach ()
to address an under-studied but practical cross-silo federated domain adaptation problem …
to address an under-studied but practical cross-silo federated domain adaptation problem …
The oarf benchmark suite: Characterization and implications for federated learning systems
This article presents and characterizes an Open Application Repository for Federated
Learning (OARF), a benchmark suite for federated machine learning systems. Previously …
Learning (OARF), a benchmark suite for federated machine learning systems. Previously …
Additively homomorphical encryption based deep neural network for asymmetrically collaborative machine learning
The financial sector presents many opportunities to apply various machine learning
techniques. Centralized machine learning creates a constraint which limits further …
techniques. Centralized machine learning creates a constraint which limits further …