Paper proposes P-ADMM for ADMM in distributed medical machine learning with differential privacy.
problem Privacy leakage in ADMM for distributed machine learning with sensitive data.
method Integrates Gaussian noise with linearly decaying variance to provide dynamic zCDP.
result P-ADMM achieves the same convergence rate as non-private ADMM while ensuring differential privacy.
Paper introduces algorithms for private decision tree learning.
problem Private decision tree learning in distributed settings.
method Proposes DP-TopDown, NoisyCounts, and LocalRNM.
result First utility guarantees for private decision tree learning.
PD-ML-Lite uses lightweight cryptography for private distributed machine learning.
problem Privacy issues in learning from distributed data.
method Applying lightweight cryptographic protocols to build learning algorithms.
result Achieves the same accuracy as non-private methods while maintaining privacy.
CodedPrivateML secures ML training data and models.
problem Training machine learning models while keeping data private.
method Data and model-theoretically private approach with parallelization.
result Significant speedup over cryptographic methods.
Paper proposes CAPE for better privacy in distributed machine learning.
problem Privacy concerns in collaborative machine learning with small datasets.
method Differential privacy with Correlation Assisted Private Estimation (CAPE).
result CAPE achieves similar performance to centralized algorithms in decentralized settings.
Locally private reinforcement learning protects individual environments from reverse engineering.
problem Protecting private information in distributed reinforcement learning environments.
method Locally differentially private algorithms that protect local agents' models from adversarial reverse engineering.
result Demonstrated that the proposed algorithm performs well under local differential privacy (LDP).
New regularizer for machine learning using private data.
problem Machine learning with private data.
method Distributionally-robust optimization with locally-differentially-private datasets.
result New regularizer for training linear regression models.
Asynchronous algorithms reduce privacy costs in distributed machine learning.
problem Privacy concerns in training machine learning models on scattered private data.
method Differentially-private asynchronous algorithms for collaborative training.
result Cost of privacy is inversely proportional to dataset size and privacy budgets.
Dynamic model considers private asset markets' complexities.
problem Understanding and optimizing private asset allocation.
method State-of-the-art dynamic model with machine learning.
result Optimal investment policies quantified over fund life.
First DP algorithm for Wasserstein barycenters on private data.
problem Computing Wasserstein barycenters on private datasets.
method Differentially private algorithms for Wasserstein barycenters.
result High-quality private barycenters with strong accuracy-privacy tradeoffs.
Gradient clipping helps private SGD converge despite potential bias.
problem Gradient clipping in private SGD can bias convergence.
method Theoretical analysis and empirical evaluation of gradient clipping effects.
result Gradient clipping can prevent convergence to stationary points and introduces bias.
The paper analyzes the trade-off between privacy and model fitness in collaborative machine learning.
problem Balancing privacy and model utility in collaborative machine learning.
method Differential privacy applied to noisy gradients in stochastic gradient descent.
result The fitness of the model is inversely proportional to the size of the datasets and privacy budget.
Alternating Direction Method of Multipliers (ADMM) is a widely used tool for machine learning in distributed settings, where a machine learning model is trained over distributed data sources through an interactive process of local computation and message passing. Such an iterative process could cause privacy concerns o…
Private machine learning framework using randomised response.
problem Private machine learning in adversarial environments.
method Randomised response for noisy data release.
result Consistent estimation of true machine learning model.
Novel PP-ADMM and IPP-ADMM algorithms improve differential privacy in distributed machine learning.
problem Privacy concerns in ADMM-based distributed machine learning.
method Proposes PP-ADMM and IPP-ADMM algorithms to provide differential privacy while improving model accuracy and convergence.
result The proposed algorithms achieve better model accuracy and convergence under the same privacy guarantee.
Study human-machine interaction with private info using offline RL.
problem Confounding bias and distributional mismatch in offline RL for human-guided interaction.
method Developed a novel identification result and OPE method to address confounding bias, and used pessimism to tackle distributional mismatch.
result Policy pair converges to optimal one at satisfactory rate under mild assumptions.
A distributed framework protects privacy while maintaining fairness in machine learning.
problem Protecting personal demographic data while ensuring fair machine learning outcomes.
method A distributed framework with private third-party data communication, ensuring privacy and fairness.
result Four fair learning methods consistently outperform existing ones in fairness and accuracy across three real-world datasets.
Researchers developed a differentially private method for computing Wasserstein distances.
problem Computing divergences between distributions while preserving privacy.
method They focused on the Sliced Wasserstein Distance and added Gaussian perturbations to make it differentially private.
result They introduced a new differentially private distance, the Smoothed Sliced Wasserstein Distance, which performs well in generative models and domain adaptation.
FPFL mitigates unfairness in private federated learning.
problem Differential privacy degrades model performance on under-represented groups.
method Extends modified method of differential multipliers to private federated learning.
result FPFL reduces unfairness in trained models on private federated learning.
Decouples data privatization from user preferences for privacy-preserving data.
problem Privacy-preserving data with user-specific private information.
method Decouples data privatization from user preferences using a Variational Autoencoder (VAE) and a generative filter trained by a GAN-type robust optimization.
result Effective privatization of data with minimal disturbance to utility, as shown by experiments on MNIST, UCI-Adult, and CelebA.
In many signal processing and machine learning applications, datasets containing private information are held at different locations, requiring the development of distributed privacy-preserving algorithms. Tensor and matrix factorizations are key components of many processing pipelines. In the distributed setting, diff…
Algorithm selects public datasets for private machine learning.
problem Choosing the most suitable public dataset for private machine learning.
method Measures gradient subspace distance between public and private datasets.
result Excess risk scales with the subspace distance between gradients.
New algorithm for differentially private distributed optimization of smooth, non-convex problems.
problem No differentially private distributed method for smooth, non-convex optimization problems.
method Smoothed normalization integrated with an error-feedback mechanism.
result Achieves superior convergence rate and first differentially private distributed optimization algorithm with provable convergence guarantees.
One-pass private sketch supports various machine learning tasks.
problem Efficiently supporting multiple machine learning tasks with differential privacy.
method Randomized contingency tables indexed with locality-sensitive hashing, constructed in one pass.
result Competitive error bounds for DP kernel density estimation, faster than existing methods.
Private learning needs more data or better features.
problem Improving differentially private machine learning performance.
method Demonstrates the need for either more private data or better features.
result Private learning requires either more data or better features.
Private distribution learning with public data, leveraging sample compression schemes.
problem Private distribution learning with public and private samples under differential privacy constraints.
method Connection to sample compression schemes and list learning.
result At least d public samples are necessary for private learnability of Gaussians in R^d.
This paper studies trade-offs in private prediction methods.
problem Leakage of training data information in machine learning predictions.
method Private training and private prediction methods with trade-offs.
result Private training methods outperform private prediction methods in various settings.
DPpack offers R tools for private data analysis and machine learning.
problem Ensuring privacy in statistical analysis and machine learning.
method Differential privacy mechanisms (Laplace, Gaussian, exponential).
result User-friendly implementation of privacy-preserving models.
This work makes federated Bayesian learning differentially private.
problem Privacy concerns in federated learning with diverse data and computational constraints.
method Modified Partitioned Variational Inference (PVI) to ensure differential privacy.
result Moderately private logistic regression models can be learned in the federated setting with similar performance to non-privately trained models.
This paper surveys techniques to personalize federated learning models.
problem Personalized models outperform shared models for some clients, reducing participation.
method Surveys recent research on personalizing federated learning models.
result Personalization techniques improve model performance for individual clients.
Paper proposes differentially private quantile regression for high-dimensional data.
problem Privacy concerns in big data with heterogeneous sensitive personal information.
method Newton-type transformation for reformulating quantile regression into an OLS problem; iterative updates for estimation; debiased estimator for inference; communication-efficient bootstrap.
result Near-optimal statistical accuracy and formal privacy guarantees achieved.
Paper improves privacy-accuracy balance in federated learning.
problem Privacy-accuracy tradeoffs in federated learning.
method Personalized federated learning with joint differential privacy.
result Coordination of local and centralized learning improves accuracy while maintaining privacy.
Public pretraining improves private model training even in extreme distribution shift scenarios.
problem Improving private model training accuracy in settings with large distribution shift.
method Empirical evaluation and theoretical explanation of public representations improving private training accuracy.
result Public representations can improve private training accuracy by up to 67% over private training from scratch in settings with large distribution shift.
Differentially private hyperparameter tuning improves privacy in machine learning.
problem Hyperparameter tuning leaks private information through selected configurations.
method Local Bayesian optimization using Gaussian Process surrogate for private gradient approximation.
result DP-GIBO converges to locally optimal hyperparameters with polynomial dimensional dependence.
This paper applies secure multi-party computation to K-means clustering to protect private data.
problem Privacy-preserving K-means clustering for distributed private data.
method Secure multi-party computation (MPC) techniques to protect private data during K-means clustering.
result Privacy-preserving K-means clustering is feasible and effective for both horizontal and vertical data distribution.
New DP mechanism SWAG-PPM improves privacy in deep learning models.
problem Differential privacy struggles with real-world distributions, especially imbalanced data.
method SWAG-PPM uses a pseudo posterior distribution to downweight high-risk records.
result SWAG-PPM outperforms DP-SGD with similar privacy budget and modest utility degradation.
A model for human-machine decision-making with private info and opacity.
problem Optimizing decisions in a human-machine system with private info and opacity.
method Formulated as a two-player learning problem, proved lower and upper bounds on optimality.
result Simple coordination strategy is nearly minimax optimal, efficient learning possible under certain assumptions.
Private learning is hard when data is long-tailed.
problem Achieving both privacy and fairness in machine learning with long-tailed data.
method Theoretical analysis and experimental validation on various datasets and algorithms.
result Relaxing overall accuracy can lead to good fairness even with strict privacy requirements.
New methods reduce bias in synthetic data for machine learning.
problem Statistical bias in synthetic data generated for privacy.
method Re-weighting strategies using privatised likelihood ratios.
result Private importance weighting enhances synthetic data utility.
Proposes private model aggregation methods to enhance machine learning models without sharing client data.
problem Lack of sufficient data for new clients in SaaS companies.
method Two private model aggregation approaches based on differential privacy techniques.
result Private model aggregation enables data utility and privacy guarantees.
AdaCliP reduces noise in private SGD training.
problem Privacy preserving machine learning over user data.
method Adaptive clipping of gradients to reduce noise in private SGD.
result AdaCliP adds less noise and improves model accuracy.
Private PGB boosts synthetic data quality using GANs and privacy techniques.
problem Differentially private GANs struggle with convergence and poor output quality.
method Combines reweighted samples from GAN training using Private Multiplicative Weights method.
result Improves synthetic data quality across various datasets and tasks.
Gradient sparsification enhances privacy-preserving machine learning models.
problem Improving performance of differentially-private machine learning models under privacy constraints.
method Gradient sparsification combined with compressed sensing and additive Laplace noise.
result Gradient sparsification can improve performance of differentially-private machine learning models for small privacy budgets.
Algorithm selects private hypothesis from unknown distribution.
problem Private selection of hypothesis from unknown distribution.
method Differentially private algorithm for hypothesis selection.
result Sample complexity of O(α2logm+αεlogm). Three tools improve practical differential privacy.
problem Challenges in applying differential privacy to real-world data.
method Three tools: sanity checks, adaptive clipping, and large-batch training.
result Improves model performance and practicality of differential privacy.
This paper operationalizes the Exponential Mechanism using Normalizing Flows for private optimization.
problem Improving privacy in machine learning while maintaining accuracy and efficiency.
method Using Normalizing Flows to approximate sampling from the Exponential Mechanism for private optimization.
result ExpM+NF provides more privacy than non-private SGD but not as much as DPSGD.
Paper tackles privacy-preserving distributed learning with ADMM.
problem Privacy-preserving distributed learning with vertically partitioned features.
method Proposes ADMM sharing framework to minimize risk over distributed features.
result Demonstrates efficient convergence and privacy guarantees for ADMM algorithms.
New DP training ensures models behave similarly at training and test time.
problem Standard SGD training leads to inconsistent model behavior at training and test time.
method Differentially-Private (DP) training ensures WYSIWYG property through distributional generalization.
result DP training guarantees high-level WYSIWYG property, improving model robustness and privacy.