This paper benchmarks privacy-preserving machine learning on medical images.
problem Ensuring privacy in medical image analysis while maintaining model accuracy.
method Comparing Local-DP and DP-SGD for differential privacy in medical imagery.
result Theoretical privacy guarantees do not fully align with real-world performance.
Privacy-preserving deep learning for medical data across distributed platforms.
problem Data leakage in medical platforms.
method Separates hidden layers; first layer local, others centralized for training.
result Improved learning performance with all data used during training.
This paper addresses privacy in federated learning for medical imaging by estimating model uncertainty.
problem Privacy concerns in federated learning for medical imaging.
method Federated Learning (FL) for collaborative model training while preserving patient data privacy.
result Accurate uncertainty estimation in federated learning for medical imaging.
Federated learning with Bayesian differential privacy offers improved privacy and accuracy.
problem Privacy in federated learning with similar data distributions.
method Bayesian differential privacy for federated learning, with improved privacy budgeting.
result Significant advantage over state-of-the-art privacy bounds, with lower noise and improved accuracy.
SAD-DPSGD improves model performance on imbalanced medical datasets like HAM10000.
problem Data leakage and imbalanced distribution in medical image classification datasets.
method SAD-DPSGD uses a linear decaying mechanism for noise and clipping thresholds to enhance performance.
result SAD-DPSGD outperforms Auto-DPSGD on HAM10000, improving accuracy by 2.15%.
Privacy-preserving deep learning for clinical data across multiple sites.
problem Training deep neural networks on sensitive clinical data requires privacy protection.
method Distributed training with differential privacy to protect patient data.
result Proven privacy guarantees for training neural networks on clinical data.
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.
SCBF preserves medical data privacy by training models without sharing inputs.
problem Privacy concerns in medical data collection and training.
method Stochastic Channel-Based Federated Learning (SCBF) with pruning.
result SCBF outperforms Federated Averaging with better performance and faster saturating speed.
PPGAN adds noise to GAN gradients to protect private data.
problem Protecting private data in GANs when training on sensitive datasets.
method Integrates differential privacy into GANs by adding noise to gradients and using Moments Accountant strategy.
result Demonstrates generation of high-quality synthetic data while maintaining privacy.
BrainTorrent uses peer-to-peer FL for medical image segmentation without a central server.
problem Lack of sufficient annotated data for personalized medical models.
method Peer-to-peer federated learning framework without a central server.
result BrainTorrent outperforms traditional server-based FL and achieves similar performance to pooled data training.
GANs help create realistic synthetic health data, boosting medical research.
problem Challenges in creating realistic synthetic health data due to private patient data.
method Generative Adversarial Networks (GANs) to learn and produce synthetic health data.
result GANs can produce realistic synthetic health data, overcoming challenges in OHD.
Large data collections required for the training of neural networks often contain sensitive information such as the medical histories of patients, and the privacy of the training data must be preserved. In this paper, we introduce a dropout technique that provides an elegant Bayesian interpretation to dropout, and show…
New method generates diverse EHR data types while maintaining privacy.
problem Privacy risks in sharing EHRs across large scales.
method Refined GAN model, feature constraints, and utility measures.
result New model preserves EHR statistics, correlations, and constraints.
GANs generate realistic ECG signals for medical research.
problem Privacy concerns in sharing medical data.
method Developed GAN architectures to generate synthetic ECG signals.
result GANs can generate diverse, structurally similar synthetic ECG signals.
Causal inference deals with identifying which random variables "cause" or control other random variables. Recent advances on the topic of causal inference based on tools from statistical estimation and machine learning have resulted in practical algorithms for causal inference. Causal inference has the potential to hav…
Federated learning enables secure meta-analysis of large medical brain datasets.
problem Privacy and legal concerns prevent direct sharing of brain imaging data across different institutions.
method Developed a federated learning framework to securely access and analyze brain data from multiple databases.
result The framework successfully analyzed brain structural relationships across various diseases and cohorts.
Generates synthetic health data from patient visits.
problem Lack of longitudinal, event-based medical data.
method Transformed longitudinal data into summary statistics, trained GAN.
result Synthetic data closely resembles real data univariately.
First differentially private off-policy evaluation method.
problem Privacy leakage in reinforcement learning using sensitive data.
method Differential privacy approach for off-policy evaluation.
result Empirically outperforms previous methods in speed of convergence.
The paper proposes simple models for interpretable predictions while maintaining privacy.
problem Balancing interpretability and privacy in complex machine learning models.
method Locally linear maps per class to approximate complex models, ensuring differential privacy.
result High classification accuracy with differentially private explanations.
FELICIA uses a centralized adversary to improve synthetic medical image generation.
problem Collaborative learning with limited and biased data in medical image analysis.
method Federated generative modeling with a centralized adversary.
result Data owners can generate high-quality synthetic images with high utility without sharing real data.
Proposes a privacy-preserving method for survival function estimation.
problem Privacy leakage in survival function estimation using sensitive data.
method Differential privacy framework applied to Kaplan-Meier estimator and related metrics.
result The method provides utility and strong privacy guarantees with real-world data.
Automated system extracts medication regimens from medical conversations.
problem Extract relevant medication information from medical conversations.
method QA task approach, combined QA and Information Extraction, data augmentation, public embeddings, pretraining.
result Improved accuracy in extracting dosage and frequency from 54.28 and 37.13 to 89.57 and 45.94.
Improved DP models with active learning on public data.
problem Differentially private models trained on sensitive data degrade performance.
method Fine-tuning DP models through active learning on public data.
result Improved accuracy for DP models while maintaining privacy guarantees.
MAGIC-Flow generates and classifies medical images with interpretability.
problem Challenges in generative modeling for medical imaging.
method Conditional multiscale normalizing flow architecture.
result MAGIC-Flow creates realistic, diverse samples and improves classification.
Paper improves deep learning privacy with new f-differential privacy framework.
problem Training neural networks on sensitive data while maintaining privacy.
method Introduced and analyzed f-differential privacy for neural networks training. result Improved privacy guarantees for neural networks training without sacrificing accuracy.
Deep neural networks with their large number of parameters are highly flexible learning systems. The high flexibility in such networks brings with some serious problems such as overfitting, and regularization is used to address this problem. A currently popular and effective regularization technique for controlling the…
Secure blockchain architectures protect data privacy in distributed learning.
problem Data privacy and trust in distributed learning across organizations.
method Adequate encryption and blockchain mechanisms ensure data privacy and trust in iterative learning.
result Secure sharing of a learned model among coalition members without revealing data.
Paper proposes sharing models instead of data for smart health predictions.
problem Sharing sensitive medical data is legally restricted and challenging.
method Train a teacher model on sensitive data, then transfer its knowledge to a student model without accessing original data.
result Student model mimics teacher model performance in making accurate predictions.
New methods protect privacy while providing accurate prediction sets.
problem Privacy-preserving conformal prediction for untrusted aggregators.
method Two LDP approaches: k-ary randomized response and binary search response.
result Finite-sample coverage guarantees and robust coverage under randomization.
Paper presents a fast, private MH algorithm for large-scale Bayesian inference.
problem Privacy-preserving Bayesian inference for large-scale data.
method Developed a novel DP-MH algorithm using minibatches.
result First exact and fast DP MH algorithm with privacy, scalability, and efficiency trade-offs.
Framework extracts symptoms from EHRs for rapid disease outbreak detection.
problem Extracting relevant data from unstructured medical texts.
method Conformal active learning for efficient data mining.
result Framework achieves strong performance with minimal manual labeling.
Paper shows federated learning can train models on private data.
problem Training predictive models on private hospital data.
method Distributed training in federated learning framework.
result Federated learning achieves comparable performance to traditional methods.
This paper adapts PATE for semantic segmentation while maintaining privacy.
problem Preserving privacy in medical machine learning, especially for sensitive information.
method Adapting PATE for semantic segmentation using low-dimensional representations and low-sensitivity queries.
result An Autoencoder-based PATE variant achieves a higher Dice coefficient for the same privacy guarantee.
Local differential privacy method protects image privacy in machine learning.
problem Protecting image privacy in machine learning models from data owners.
method Locally perturbs image representations before releasing them to the data user.
result Proposes DCAConv, an image feature extractor that maintains high data utility while preserving privacy.
Differentially private geodesic regression for non-Euclidean data.
problem Protecting sensitive data on non-linear spaces like manifolds.
method K-Norm Gradient (KNG) mechanism for Riemannian manifolds.
result Theoretical bounds for sensitivity of geodesic regression parameters.
Survey on securing ML for healthcare, addressing privacy and robustness issues.
problem Security and robustness challenges in healthcare ML/DL applications.
method Overview of security and privacy methods for ML in healthcare.
result Discussion of current research challenges and future directions.
Study shows federated learning can segment brain tumors without data sharing.
problem Lack of sufficient medical data for deep learning models.
method Federated learning for multi-institutional collaboration without sharing patient data.
result Federated semantic segmentation models perform similarly to those trained with shared data.
Enhances generative model for clinical data privacy and accuracy.
problem Data privacy in electronic patient records.
method Improves a time-series generative model with privacy safeguards.
result DP-TimeGAN achieves a mean authenticity of 0.778 on the CKD dataset.
Efficient method defends privacy in federated learning without accuracy loss.
problem Privacy attacks on federated learning by reconstructing and identifying local data.
method Random noise perturbation method that allows recovery of true gradients.
result Strong privacy protection without sacrificing learning accuracy.
Paper evaluates security of deep learning from side-channel attacks.
problem Security of deep learning algorithms from side-channel attacks.
method Uses hardware performance counters to test for information leakage in CNNs.
result Shows alarms for potential data privacy breaches in CNNs.
Differential privacy is a leading protection setting, focused by design on individual privacy. Many applications, in medical / pharmaceutical domains or social networks, rather posit privacy at a group level, a setting we call integral privacy. We aim for the strongest form of privacy: the group size is in particular n…
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.
GS-WGAN sanitizes sensitive data for machine learning with improved privacy and model quality.
problem Lack of privacy in sensitive data hinders machine learning applications.
method Gradient-sanitized Wasserstein Generative Adversarial Networks (GS-WGAN).
result GS-WGAN generates more informative samples and outperforms state-of-the-art approaches.
DP-GD improves CNN training accuracy with privacy, especially with low signal-to-noise ratios.
problem Privacy-preserving training of neural networks with crowdsourced data.
method Differentially private gradient descent (DP-GD) algorithm applied to two-layer CNNs.
result DP-GD can achieve superior generalization performance compared to GD, especially with low signal-to-noise ratios.
CorGAN generates synthetic healthcare records while preserving privacy.
problem Generating realistic synthetic healthcare records while maintaining privacy.
method Combining Convolutional Generative Adversarial Networks and Convolutional Autoencoders to capture correlations between medical features.
result CorGAN generates synthetic data with performance similar to real data in various ML settings.
RDP-GAN improves GAN privacy by adding random noises to loss function.
problem Protecting sensitive information in GANs while maintaining quality of generated samples.
method Integrates Rényi-differential privacy into GAN training process by adding random noises to loss function.
result Achieves better privacy protection with high-quality samples compared to existing methods.
The rapid adoption of machine learning has increased concerns about the privacy implications of machine learning models trained on sensitive data, such as medical records or other personal information. To address those concerns, one promising approach is Private Aggregation of Teacher Ensembles, or PATE, which transfer…
A new mechanism for differentially private Fréchet mean on SPD matrices.
problem Privacy-preserving statistical summaries for SPD matrices.
method Tangent Gaussian mechanism for log-Euclidean metric.
result Significantly better utility and computational efficiency.