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Privacy-Preserving Distributed Expectation Maximization for Gaussian
  Mixture Model using Subspace Perturbation

Privacy-Preserving Distributed Expectation Maximization for Gaussian Mixture Model using Subspace Perturbation

16 September 2022
Qiongxiu Li
Jaron Skovsted Gundersen
K. Tjell
R. Wisniewski
M. G. Christensen
    FedML
ArXivPDFHTML

Papers citing "Privacy-Preserving Distributed Expectation Maximization for Gaussian Mixture Model using Subspace Perturbation"

4 / 4 papers shown
Title
Privacy-Preserving Distributed Processing: Metrics, Bounds, and
  Algorithms
Privacy-Preserving Distributed Processing: Metrics, Bounds, and Algorithms
Qiongxiu Li
Jaron Skovsted Gundersen
Richard Heusdens
M. G. Christensen
46
33
0
02 Sep 2020
Privacy Preserving Machine Learning: Threats and Solutions
Privacy Preserving Machine Learning: Threats and Solutions
Mohammad Al-Rubaie
Jerome Chang
49
336
0
27 Mar 2018
Deep Models Under the GAN: Information Leakage from Collaborative Deep
  Learning
Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning
Briland Hitaj
G. Ateniese
Fernando Perez-Cruz
FedML
111
1,401
0
24 Feb 2017
Deep Learning with Differential Privacy
Deep Learning with Differential Privacy
Martín Abadi
Andy Chu
Ian Goodfellow
H. B. McMahan
Ilya Mironov
Kunal Talwar
Li Zhang
FedML
SyDa
191
6,113
0
01 Jul 2016
1