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Found 7 papers, 1 papers with code
Training generative models from privatized data
Local differential privacy (LDP) is a powerful method for privacy-preserving data collection.
The Poisson binomial mechanism for secure and private federated learning
Unlike previous discrete DP schemes based on additive noise, our mechanism encodes local information into a parameter of the binomial distribution, and hence the output distribution is discrete with bounded support.
Breaking The Dimension Dependence in Sparse Distribution Estimation under Communication Constraints
For the interactive setting, we propose a novel tree-based estimation scheme and show that the minimum sample-size needed to achieve dimension-free convergence can be further reduced to $n^*(s, d, b) = \tilde{O}\left( {s^2\log^2 d}/{2^b} \right)$.
Breaking the Communication-Privacy-Accuracy Trilemma
In particular, we consider the problems of mean estimation and frequency estimation under $\varepsilon$-local differential privacy and $b$-bit communication constraints.
Lower Bounds and a Near-Optimal Shrinkage Estimator for Least Squares using Random Projections
An upper bound on the expected error of this estimator is derived, which is smaller than the error of the classical Gaussian sketch solution for any given data.
Advances and Open Problems in Federated Learning
FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches.
Phase Retrieval via Incremental Truncated Wirtinger Flow
In this paper, we present an algorithm to solve a nonconvex formulation of the phase retrieval problem, that we call $\textit{Incremental Truncated Wirtinger Flow}$.
