A Verifiable Privacy-Preserving Federated Learning Framework Against Collusion Attacks.

A Privacy-Preserving Federated Learning Framework With Lightweight and Fair in IoT.

Privacy-preserving data integration scheme in industrial robot system based on fog computing and edge computing.

Verifiable Privacy-Preserving Federated Learning Under Multiple Encrypted Keys.

Privacy-Preserving Convolutional Neural Network Classification Scheme With Multiple Keys.

PEPFL: A framework for a practical and efficient privacy-preserving federated learning.

Privacy-preserving multi-party deep learning based on homomorphic proxy re-encryption.

PPeFL: Privacy-Preserving Edge Federated Learning With Local Differential Privacy.

PLDP-FL: Federated Learning with Personalized Local Differential Privacy.

Privacy-Preserving Multi-Class Support Vector Machine Model on Medical Diagnosis.

Privacy-Preserving Classification Scheme Based on Support Vector Machine.

PDLHR: Privacy-Preserving Deep Learning Model With Homomorphic Re-Encryption in Robot System.

Toward practical privacy-preserving linear regression.

Cryptanalysis and Improvement of DeepPAR: Privacy-Preserving and Asynchronous Deep Learning for Industrial IoT.

Privacy-preserving convolutional neural network prediction with low latency and lightweight users.

Verifiable privacy-preserving association rule mining using distributed decryption mechanism on the cloud.

Efficient Private Information Retrieval Protocol with Homomorphically Computing Univariate Polynomials.

Privacy-preserving image multi-classification deep learning model in robot system of industrial IoT.

A threshold hybrid encryption method for integrity audit without trusted center.

Design of Three-dimensional Interchange Network Based on IPv4/IPv6 Network.