Privacy-Preserving Distributed Processing: Metrics, Bounds, and Algorithms

2 September 2020

Papers citing "Privacy-Preserving Distributed Processing: Metrics, Bounds, and Algorithms"

8 / 8 papers shown

Title
Provable Privacy Advantages of Decentralized Federated Learning via Distributed Optimization Wenrui Yu Qiongxiu Li Milan Lopuhaä-Zwakenberg Mads Græsbøll Christensen Richard Heusdens FedML 40 4 0 12 Jul 2024
Distributed Harmonization: Federated Clustered Batch Effect Adjustment and Generalization Hoang Bao Yijiang Pang Siqi Liang Liang Zhan Paul Thompson Jiayu Zhou FedML 48 1 0 23 May 2024
Topology-Dependent Privacy Bound For Decentralized Federated Learning Qiongxiu Li Wenrui Yu Changlong Ji Richard Heusdens 37 3 0 13 Dec 2023
Adaptive Differentially Quantized Subspace Perturbation (ADQSP): A Unified Framework for Privacy-Preserving Distributed Average Consensus Qiongxiu Li Jaron Skovsted Gundersen Milan Lopuhaä-Zwakenberg Richard Heusdens 28 11 0 13 Dec 2023
Privacy-Preserving Distributed Expectation Maximization for Gaussian Mixture Model using Subspace Perturbation Qiongxiu Li Jaron Skovsted Gundersen K. Tjell R. Wisniewski M. G. Christensen FedML 12 11 0 16 Sep 2022
Investigation of Alternative Measures for Mutual Information Bulut Kuskonmaz Jaron Skovsted Gundersen R. Wisniewski 26 4 0 02 Feb 2022
Secure PAC Bayesian Regression via Real Shamir Secret Sharing Jaron Skovsted Gundersen Bulut Kuskonmaz R. Wisniewski 41 1 0 23 Sep 2021
SoK: Differential Privacies Damien Desfontaines Balázs Pejó 44 122 0 04 Jun 2019