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Found 4 papers, 2 papers with code
Privacy-Preserving Distributed Optimisation using Stochastic PDMM
The main idea is to exploit a certain structure in the update equations for noise insertion such that the private data is protected without compromising the algorithm's accuracy.
Privacy-Preserving Distributed Expectation Maximization for Gaussian Mixture Model using Subspace Perturbation
Privacy has become a major concern in machine learning.
On the Privacy Effect of Data Enhancement via the Lens of Memorization
We demonstrate that the generalization gap and privacy leakage are less correlated than those of the previous results.
Privacy-Preserving Distributed Optimization via Subspace Perturbation: A General Framework
We therefore propose to insert noise in the non-convergent subspace through the dual variable such that the private data are protected, and the accuracy of the desired solution is completely unaffected.
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