New privacy-preserving method for conformal prediction without splitting data.
problem Privacy and uncertainty quantification in data-driven decision making.
method Proposes a full-data privacy-preserving conformal prediction framework using differential privacy.
result Demonstrates improved prediction sets compared to split-based private baselines.
Bayesian VFLMSP improves multimodal survival prediction with privacy.
problem Privacy and reliability in multimodal time-to-event prediction.
method Bayesian Vertical Federated Learning (VFL) with differential privacy.
result Consistent improvements in C-index compared to existing methods.
A framework for private prediction sets using conformal prediction and differential privacy.
problem Jointly addressing reliability and privacy in machine learning predictions.
method Split conformal prediction with privatized quantile subroutine.
result Private prediction sets can be generated from privately-trained models.
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.
Privacy-preserving machine learning methods add randomness, leading to varying predictions.
problem Privacy-preserving machine learning methods add randomness, leading to varying predictions.
method The study analyzes three DP-ensuring algorithms: output perturbation, objective perturbation, and DP-SGD.
result The degree of predictive multiplicity rises as the level of privacy increases, and is unevenly distributed across individuals and demographic groups.
Study on privacy-preserving health care models that sacrifice accuracy for data protection.
problem Privacy-preserving models in health care neglect data from the tails, reducing accuracy for small groups.
method Used state-of-the-art differentially private learning methods for clinical prediction tasks.
result Privacy-preserving models in health care exhibit steep tradeoffs between privacy and utility, and disproportionately influence large demographic groups.
FEDMD-NFDP improves federated learning privacy without sacrificing performance.
problem Privacy leakage in federated learning when sharing predictions.
method Noise-Free Differential Privacy (NFDP) applied to federated model distillation.
result FEDMD-NFDP achieves comparable utility and privacy guarantees.
New algorithms improve privacy-preserving data release using external predictions.
problem Privacy-preserving data release with improved utility using external information.
method Learning-augmented algorithms for multiple quantile release.
result Error guarantees scale with prediction quality, almost recovering state-of-the-art guarantees.
Link prediction (LP) algorithms propose to each node a ranked list of nodes that are currently non-neighbors, as the most likely candidates for future linkage. Owing to increasing concerns about privacy, users (nodes) may prefer to keep some of their connections protected or private. Motivated by this observation, our …
FedGRU uses federated learning to predict traffic flow accurately while preserving user privacy.
problem Developing accurate traffic flow prediction while protecting user privacy.
method Federated Learning, Secure Parameter Aggregation, Joint Announcement Protocol, Ensemble Clustering.
result FedGRU achieves 90.96% higher prediction accuracy than advanced deep learning models.
Gaussian processes with differential privacy protect both inputs and outputs.
problem Previous DP methods only protected model outputs, not inputs.
method Sparse GP with private variational approximation, adjusting covariance for DP noise.
result Accurate models can be produced under strong privacy protection with sufficient data.
We study the problem of privacy-preserving machine learning (PPML) for ensemble methods, focusing our effort on random forests. In collaborative analysis, PPML attempts to solve the conflict between the need for data sharing and privacy. This is especially important in privacy sensitive applications such as learning pr…
Private cancer prediction model trained on federated genomic data.
problem Train a private cancer prediction model on federated genomic data.
method Differentially private federated learning (FL) for genomic cancer prediction.
result Ranked 3rd in a competition for private cancer prediction.
DP-BNNs improve accuracy, privacy, and reliability in neural networks.
problem Balancing privacy and accuracy in neural networks.
method Proposed three DP-BNNs: DP-SGLD, DP-BBP, and DP-MC Dropout.
result DP-SGLD achieves high accuracy under strong privacy guarantees.
New algorithms improve prediction with expert advice under local differential privacy.
problem Predicting expert advice with privacy constraints.
method Design of two new algorithms: RW-AdaBatch and RW-Meta, leveraging limited-switching behavior and random walks.
result RW-Meta outperforms classical and central DP algorithms by 1.5-3x on predicting hospital COVID patient densities.
To meet the standard of differential privacy, noise is usually added into the original data, which inevitably deteriorates the predicting performance of subsequent learning algorithms. In this paper, motivated by the success of improving predicting performance by ensemble learning, we propose to enhance privacy-preserv…
New algorithms improve online prediction from experts with privacy constraints.
problem Online prediction from experts under privacy constraints.
method Proposed and analyzed new algorithms for approximate and pure differential privacy.
result Achieved improved regret bounds for various adversaries.
Paper protects privacy and fairness in deep learning models.
problem Ensuring fairness in deep learning models while protecting sensitive data.
method Uses differential privacy and Lagrangian duality to design fair predictors.
result Demonstrates improved model performance on prediction tasks.
A new DP approach for Conformal Prediction using quantile search.
problem Privacy leakage in uncertainty quantification methods like Conformal Prediction.
method Private Conformity via Quantile Search (P-COQS) using randomized binary search.
result The approach targets the desired (1−α)-level of coverage with slight under-covering. New federated f-differential privacy for collaborative learning.
problem Privacy in federated learning.
method Introducing federated f-differential privacy and proposing a generic private federated learning framework. result Proves federated f-differential privacy provides privacy guarantee on each record of one client's data. New federated conformal prediction method addresses label shift for uncertainty quantification.
problem Label shift in federated learning and its impact on uncertainty quantification.
method Quantile regression-based federated conformal prediction method with privacy constraints.
result Method provides valid coverage of prediction sets and differential privacy guarantees.
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.
Paper removes sensitive data from IoT and Big Data for privacy.
problem Privacy concerns in IoT and Big Data.
method Develops new supervised and adversarial learning methods to remove sensitive data.
result Models maintain predictive model utility while making sensitive predictions ineffective.
Paper improves speaker verification with federated learning and differential privacy.
problem Improving speaker verification accuracy using private data.
method Combining federated learning and differential privacy to train an auxiliary model that predicts vocal characteristics.
result 6% relative improvement in equal error rate over a baseline system.
Differentially private conformal prediction improves statistical efficiency.
problem Quantifying uncertainty in private data analysis.
method Introducing differential conformal prediction and developing Differentially Private Conformal Prediction (DPCP).
result DPCP produces tighter prediction sets than existing private split conformal approaches.
Proposes PFWCP for multi-agent tasks with privacy and validity guarantees.
problem Challenges in uncertainty quantification for multi-agent settings.
method Personalized federated weighted conformal prediction (PFWCP) combining local density ratio weighting and weighted quantile aggregation.
result Asymptotically valid coverage guarantees for each agent in heterogeneous settings.
DPConvCNP learns to predict private data accurately and privately.
problem Balancing privacy and accuracy in machine learning models.
method Meta-learning combined with improved DP mechanism.
result DPConvCNP outperforms DP GP baseline, especially on non-Gaussian data.
New method improves privacy risk evaluation of machine learning models.
problem Machine learning models can be vulnerable to membership inference attacks.
method Proposed new inference attack method based on prediction entropy, and introduced privacy risk score metric.
result Existing defense approaches are not as effective as previously reported.
The introduction of data analytics into medicine has changed the nature of patient treatment. In this, patients are asked to disclose personal information such as genetic markers, lifestyle habits, and clinical history. This data is then used by statistical models to predict personalized treatments. However, due to pri…
Machine learning models, especially deep neural networks have been shown to be susceptible to privacy attacks such as membership inference where an adversary can detect whether a data point was used for training a black-box model. Such privacy risks are exacerbated when a model's predictions are used on an unseen data …
Study improves privacy-preserving online prediction from experts with speed-ups.
problem Privacy-preserving online prediction from experts with speed-ups.
method Differentially private federated online prediction algorithms.
result Achieves m-fold regret speed-up with low-loss expert in federated setting. In this paper, we apply machine learning to distributed private data owned by multiple data owners, entities with access to non-overlapping training datasets. We use noisy, differentially-private gradients to minimize the fitness cost of the machine learning model using stochastic gradient descent. We quantify the qual…
Differential privacy of Gaussian process posterior sampling
problem Privacy of posterior sample paths from Gaussian process
method Intrinsic randomness yields DP guarantees
result Intrinsic randomness yields DP guarantees
Study creates a multimodal learning framework for CVD risk prediction.
problem Predicting cardiovascular disease risk in diverse populations.
method Combines cross modal transformers, graph neural networks, and causal representation learning.
result Model predicts personalized CVD risk with causal invariance across subpopulations.
Stock price prediction is important for value investments in the stock market. In particular, short-term prediction that exploits financial news articles is promising in recent years. In this paper, we propose a novel deep neural network DP-LSTM for stock price prediction, which incorporates the news articles as hidden…
Machine learning models leak information about the datasets on which they are trained. An adversary can build an algorithm to trace the individual members of a model's training dataset. As a fundamental inference attack, he aims to distinguish between data points that were part of the model's training set and any other…
Deep learning models are often trained on datasets that contain sensitive information such as individuals' shopping transactions, personal contacts, and medical records. An increasingly important line of work therefore has sought to train neural networks subject to privacy constraints that are specified by differential…
We detail a new framework for privacy preserving deep learning and discuss its assets. The framework puts a premium on ownership and secure processing of data and introduces a valuable representation based on chains of commands and tensors. This abstraction allows one to implement complex privacy preserving constructs …
Differential Privacy (DP) provides strong guarantees on the risk of compromising a user's data in statistical learning applications, though these strong protections make learning challenging and may be too stringent for some use cases. To address this, we propose element level differential privacy, which extends differ…
Retraining with predicted labels improves model accuracy in noisy settings.
problem Improving model accuracy with noisy or corrupted labels.
method Retraining with predicted hard labels in a linearly separable binary classification setting.
result Retraining with predicted labels can increase model accuracy, as proven theoretically.
This paper analyzes privacy risks in neural network pruning and proposes a defense mechanism.
problem Privacy risks in neural network pruning due to membership inference attacks.
method Investigates the impact of pruning on prediction divergence and proposes a self-attention membership inference attack.
result Proposed defense mechanism mitigates privacy risks while maintaining sparsity and accuracy.
Ensuring differential privacy of models learned from sensitive user data is an important goal that has been studied extensively in recent years. It is now known that for some basic learning problems, especially those involving high-dimensional data, producing an accurate private model requires much more data than learn…
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.
Combining differential privacy and federated learning improves data security.
problem Ensuring privacy in distributed machine learning.
method Integrating differential privacy with federated learning protocols.
result Achieved comparable prediction accuracy to non-distributed learning while maintaining strict privacy.
TVineSynth generates synthetic data to balance privacy and utility.
problem Balancing privacy and utility in synthetic data generation.
method Uses vine copula with truncation to control privacy and utility trade-off.
result Achieves superior privacy-utility balance compared to competitors.
One-shot federated learning method for prediction sets.
problem Constructing prediction sets in a one-shot federated learning setting.
method Quantile-of-quantiles estimator for one-shot federated learning with privacy.
result Achieves desired coverage and length similar to centralized setting.
Machine learning models can be used for pattern recognition in medical data in order to improve patient outcomes, such as the prediction of in-hospital mortality. Deep learning models, in particular, require large amounts of data for model training. However, the data is often collected at different hospitals and sharin…
Interpretable predictions, where it is clear why a machine learning model has made a particular decision, can compromise privacy by revealing the characteristics of individual data points. This raises the central question addressed in this paper: Can models be interpretable without compromising privacy? For complex big…