End-to-end privacy preserving deep learning on multi-institutional medical imaging
Nature Machine Intelligence, 2021 Using large, multi-national datasets for high-performance medical imaging AI systems requires innovation in privacy-preserving machine learning so models can train on sensitive data without requiring data transfer.
Georgios Kaissis +13 more
semanticscholar +1 more source
Privacy-Preserving Aggregation in Federated Learning: A Survey [PDF]
IEEE Transactions on Big Data, 2022 Over the recent years, with the increasing adoption of Federated Learning (FL) algorithms and growing concerns over personal data privacy, Privacy-Preserving Federated Learning (PPFL) has attracted tremendous attention from both academia and industry ...
Ziyao Liu +5 more
semanticscholar +1 more source
Privacy-Preserving Machine Learning With Fully Homomorphic Encryption for Deep Neural Network [PDF]
IEEE Access, 2021 Fully homomorphic encryption (FHE) is a prospective tool for privacy-preserving machine learning (PPML). Several PPML models have been proposed based on various FHE schemes and approaches.
Joon-Woo Lee +10 more
semanticscholar +1 more source
Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation [PDF]
The Web Conference, 2022 Cross Domain Recommendation (CDR) has been popularly studied to alleviate the cold-start and data sparsity problem commonly existed in recommender systems. CDR models can improve the recommendation performance of a target domain by leveraging the data of
Chaochao Chen +5 more
semanticscholar +1 more source
Privacy‐preserving federated learning based on multi‐key homomorphic encryption [PDF]
International Journal of Intelligent Systems, 2021 With the advance of machine learning and the Internet of Things (IoT), security and privacy have become critical concerns in mobile services and networks. Transferring data to a central unit violates the privacy of sensitive data.
Jing Ma, Si-Ahmed Naas, S. Sigg, X. Lyu
semanticscholar +1 more source
Privacy Preserving and Resilient RPKI [PDF]
IEEE INFOCOM 2021 - IEEE Conference on Computer Communications, 2021 Resource Public Key Infrastructure (RPKI) is vital to the security of inter-domain routing. However, RPKI enables Regional Internet Registries (RIRs) to unilaterally takedown IP prefixes - indeed, such attacks have been launched by nation-state adversaries. The threat of IP prefix takedowns is one of the factors hindering RPKI adoption.
Kris Shrishak, Haya Shulman
openaire +3 more sources
Privacy Preserving Collaborative Machine Learning [PDF]
EAI Endorsed Transactions on Security and Safety, 2021 Collaborative machine learning is a promising paradigm that allows multiple participants to jointly train a machine learning model without exposing their private datasets to other parties.
Zheyuan Liu, Rui Zhang
doaj +1 more source
Privacy-Preserving Federated Learning Using Homomorphic Encryption
Applied Sciences, 2022 Federated learning (FL) is a machine learning technique that enables distributed devices to train a learning model collaboratively without sharing their local data.
Jaehyoung Park, Hyuk-Kyu Lim
semanticscholar +1 more source
A federated graph neural network framework for privacy-preserving personalization [PDF]
Nature Communications, 2021 Graph neural network (GNN) is effective in modeling high-order interactions and has been widely used in various personalized applications such as recommendation.
Chuhan Wu +4 more
semanticscholar +1 more source
My Private Cloud Overview: A Trust, Privacy and Security Infrastructure for the Cloud [PDF]
, 2011 Based on the assumption that cloud providers can be trusted (to a certain extent) we define a trust, security and privacy preserving infrastructure that relies on trusted cloud providers to operate properly.
Alhadeff, Joseph +4 more
core +3 more sources