Adversarial interference and its mitigations in privacy-preserving collaborative machine learning
Despite the rapid increase of data available to train machine-learning algorithms in many
domains, several applications suffer from a paucity of representative and diverse data. The …
domains, several applications suffer from a paucity of representative and diverse data. The …
Privacy preservation in Distributed Deep Learning: A survey on Distributed Deep Learning, privacy preservation techniques used and interesting research directions
E Antwi-Boasiako, S Zhou, Y Liao, Q Liu… - Journal of Information …, 2021 - Elsevier
Abstract Distributed or Collaborative Deep Learning, has recently gained more recognition
due to its major advantage of allowing two or more learning participants to contribute and …
due to its major advantage of allowing two or more learning participants to contribute and …
{XONN}:{XNOR-based} oblivious deep neural network inference
Advancements in deep learning enable cloud servers to provide inference-as-a-service for
clients. In this scenario, clients send their raw data to the server to run the deep learning …
clients. In this scenario, clients send their raw data to the server to run the deep learning …
Machine learning security: Threats, countermeasures, and evaluations
Machine learning has been pervasively used in a wide range of applications due to its
technical breakthroughs in recent years. It has demonstrated significant success in dealing …
technical breakthroughs in recent years. It has demonstrated significant success in dealing …
A framework for collaborative learning in secure high-dimensional space
As the amount of data generated by the Internet of the Things (IoT) devices keeps
increasing, many applications need to offload computation to the cloud. However, it often …
increasing, many applications need to offload computation to the cloud. However, it often …
A Generic Cryptographic Deep-Learning Inference Platform for Remote Sensing Scenes
Q Chen, Y Wu, X Wang, ZL Jiang… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
Deep learning plays an essential role in multidisciplinary research of remote sensing. We
will encounter security problems during the data acquisition, processing, and result …
will encounter security problems during the data acquisition, processing, and result …
Edge-enabled distributed deep learning for 5g privacy protection
Due to the limited storage and computing power, edge devices at the network edge cannot
train deep learning models locally. Traditional deep learning training requires users to …
train deep learning models locally. Traditional deep learning training requires users to …
Sealing neural network models in secure deep learning accelerators
Deep learning (DL) accelerators are increasingly deployed on edge devices to support fast
local inferences. However, they suffer from a new security problem, ie, being vulnerable to …
local inferences. However, they suffer from a new security problem, ie, being vulnerable to …
Refacing Defaced MRI with PixelCNN
Privacy protection is one of the most crucial factors when sharing MR images between
researchers. There are many defacing software programs that can blur or remove the face of …
researchers. There are many defacing software programs that can blur or remove the face of …
PyHENet: A Generic Framework for Privacy-Preserving DL Inference Based on Fully Homomorphic Encryption
Q Chen, L Yao, Y Wu, X Wang, W Zhang… - … Conference on Data …, 2022 - ieeexplore.ieee.org
Deep learning inference provides inference service by service provider with model for client
with input of personal data. Due to the huge commercial value inside, on one hand, both …
with input of personal data. Due to the huge commercial value inside, on one hand, both …