SMOTE-DP enhances synthetic data privacy without sacrificing utility.
problem Balancing privacy and utility in synthetic data generation.
method Integrating SMOTE with differential privacy mechanisms.
result SMOTE-DP produces synthetic data that is both private and useful.
While many solutions for privacy-preserving convex empirical risk minimization (ERM) have been developed, privacy-preserving nonconvex ERM remains a challenge. We study nonconvex ERM, which takes the form of minimizing a finite-sum of nonconvex loss functions over a training set. We propose a new differentially private…
New method preserves privacy while improving machine learning accuracy.
problem Privacy-preserving machine learning for daily data.
method Compressive Privacy and multi-kernel method.
result Improved utility classification accuracy with privacy preservation.
Preserving the privacy of individuals by protecting their sensitive attributes is an important consideration during microdata release. However, it is equally important to preserve the quality or utility of the data for at least some targeted workloads. We propose a novel framework for privacy preservation based on the …
Theory integrates loss aversion into expected utility for monetary returns.
problem Modeling loss aversion in expected utility theory.
method Develops state-dependent linear utility functions incorporating loss aversion.
result Contracts from monopolists in insurance markets.
Accurately learning from user data while providing quantifiable privacy guarantees provides an opportunity to build better ML models while maintaining user trust. This paper presents a formal approach to carrying out privacy preserving text perturbation using the notion of dx-privacy designed to achieve geo-indistingui…
Pruning neural networks adds differential privacy noise, preserving data utility.
problem Achieving differential privacy in neural networks without sacrificing data utility.
method Proving equivalence between pruning and adding differential privacy noise to hidden-layer activations.
result Pruning can be a more effective alternative to adding differential privacy noise for neural networks.
Paper proposes dp-VAE for preserving spatial context in gene expression data.
problem Inaccessibility of spatial context in single-cell gene expression data.
method Generic representation learning and transfer learning framework with a distance-preserving regularizer.
result dp-VAE effectively reconstructs and imputes spatial context from gene expression data.
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.
With a rapidly increasing number of devices connected to the internet, big data has been applied to various domains of human life. Nevertheless, it has also opened new venues for breaching users' privacy. Hence it is highly required to develop techniques that enable data owners to privatize their data while keeping it …
Paper improves privacy in SGD with low noise, achieving optimal risk rates.
problem Privacy-preserving machine learning with good performance.
method Differentially private SGD with low-noise analysis.
result Achieves optimal excess risk rates for non-smooth losses.
New method calibrates noise for attack risk, improving ML model accuracy.
problem Improving accuracy of privacy-preserving ML models while maintaining privacy.
method Directly calibrates noise scale to a desired attack risk level, bypassing the standard ε-calibration. result Significantly decreases noise scale, leading to increased utility at the same risk level.
Paper develops a federated learning method to protect privacy without sacrificing model utility.
problem Privacy leakage in federated learning due to information exchange between edge devices and server.
method Combines local gradient perturbation, secure aggregation, and zCDP for privacy protection.
result Demonstrates superior trade-off between privacy and model utility through extensive experiments.
Additive noise protects privacy in releasing datasets for SVM classification.
problem Maintaining privacy in releasing datasets for SVM classification.
method Additive noise applied to obfuscate the dataset, optimizing privacy and utility measures.
result Optimal noise distribution ensures close classifier performance between original and obfuscated datasets, achieving local differential privacy.
Hybrid approach protects privacy while analyzing smart meter data.
problem Privacy concerns in AMI data analysis under CPUC regulations.
method Anonymization, differential privacy, federated learning, synthetic data, cryptography.
result Comprehensive privacy-preserving analytics framework for AMI data.
FairJob dataset for job recommendations in advertising, preserving fairness and utility.
problem Fairness in online job recommendations for advertising.
method Anonymized dataset with proxy for sensitive attributes, fairness metric computation.
result Demonstrated potential improvements in fairness with trade-offs in utility.
Flow matching improves synthetic data generation for tabular data.
problem Generating synthetic tabular data while preserving privacy.
method Flow matching (FM) and Variational FM for tabular data synthesis.
result FM outperforms diffusion baselines in tabular data synthesis.
Differential privacy is a mathematical framework for privacy-preserving data analysis. Changing the hyperparameters of a differentially private algorithm allows one to trade off privacy and utility in a principled way. Quantifying this trade-off in advance is essential to decision-makers tasked with deciding how much p…
Domain adaptation aims to assist the modeling tasks of the target domain with knowledge of the source domain. The two domains often lie in different feature spaces due to diverse data collection methods, which leads to the more challenging task of heterogeneous domain adaptation (HDA). A core issue of HDA is how to pre…
We design two mechanisms for the recommender system to collect user ratings. One is modified Laplace mechanism, and the other is randomized response mechanism. We prove that they are both differentially private and preserve the data utility.
Over the past few decades, we have witnessed a large family of algorithms that have been designed to provide different solutions to the problem of dimensionality reduction (DR). The DR is an essential tool to excavate the important information from the high-dimensional data by mapping the data to a low-dimensional subs…
Active learning holds promise of significantly reducing data annotation costs while maintaining reasonable model performance. However, it requires sending data to annotators for labeling. This presents a possible privacy leak when the training set includes sensitive user data. In this paper, we describe an approach for…
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.
Guaranteeing a certain level of user privacy in an arbitrary piece of text is a challenging issue. However, with this challenge comes the potential of unlocking access to vast data stores for training machine learning models and supporting data driven decisions. We address this problem through the lens of dx-privacy, a…
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.
Proposes eDNNs and iDNNs for deep learning on manifolds.
problem Deep learning on manifolds with geometric preservation and intrinsic geometry incorporation.
method Intrinsic and extrinsic deep neural networks (iDNNs and eDNNs) with geometric embeddings and maps.
result Empirical risk minimizers of eDNNs and iDNNs converge optimally.
AdOBEst-LDP improves privacy-preserving frequency estimation for categorical data.
problem Estimating categorical distributions online while preserving privacy.
method AdOBEst-LDP uses adaptive randomized response mechanism to enhance future data utility.
result AdOBEst-LDP selects optimal subset for LDP mechanism with high probability.
Proposes a privacy-preserving recommendation system using matrix factorization and differential privacy.
problem Privacy leakage in recommendation systems when anonymizing user data is not sufficient.
method Uses matrix factorization and differential privacy via the Gaussian mechanism.
result Demonstrates excellent utility for privacy-preserving recommendation systems.
Pipeline parallelism (PP) when training neural networks enables larger models to be partitioned spatially, leading to both lower network communication and overall higher hardware utilization. Unfortunately, to preserve the statistical efficiency of sequential training, existing PP techniques sacrifice hardware efficien…
Cloak identifies essential features to preserve prediction privacy without provider collaboration.
problem Discovering the subset of features necessary for a prediction task.
method Gradient-based perturbation maximization method to identify essential features, followed by suppression of the rest using utility-preserving constant values.
result Cloak reduces mutual information between input and sifted representations by 85.01% with negligible utility loss.
Privacy-preserving synthetic data from EHRs for learning and inference.
problem Sharing sensitive EHR data while maintaining patient privacy.
method Differentially private normalizing flows for density estimation and variational inference.
result Privacy-preserving synthetic data can yield good utility at a reasonable privacy cost.
This work proposes a novel privacy-preserving method for synthetic replacement of sensitive data.
problem Privacy-preserving transformations of sensitive data.
method Adversarial representation learning for synthetic replacement of private attributes.
result Our method provides stronger privacy and better utility than previous methods.
Paper presents a privacy-preserving method for dynamic assortment selection.
problem Personalized assortment recommendations with data privacy concerns.
method Perturbed upper confidence bound method integrating calibrated noise.
result Policy satisfies Joint Differential Privacy (JDP) with near-optimal regret bound.
New approach preserves privacy in high-dimensional data using representation learning.
problem Preserving privacy in high-dimensional data collection.
method Adapting representation learning techniques to add noise to low-dimensional data representations.
result Significantly outperforms current LDP mechanisms in downstream model learning.
Many reinforcement learning applications involve the use of data that is sensitive, such as medical records of patients or financial information. However, most current reinforcement learning methods can leak information contained within the (possibly sensitive) data on which they are trained. To address this problem, w…
Releasing full data records is one of the most challenging problems in data privacy. On the one hand, many of the popular techniques such as data de-identification are problematic because of their dependence on the background knowledge of adversaries. On the other hand, rigorous methods such as the exponential mechanis…
Efficiently preserves privacy in logistic regression for IoT data.
problem Balancing data privacy and utility in collaborative learning.
method Matrix encryption approach for secure multi-party computation.
result Proposes a privacy-preserving logistic regression model with fast convergence.
Generative text classifiers are most vulnerable to membership inference attacks.
problem Privacy threat from Membership Inference Attacks on generative text classifiers.
method Comprehensive empirical evaluation of generative, discriminative, and pseudo-generative classifiers across various datasets.
result Generative classifiers explicitly modeling P(X,Y) are most vulnerable to membership leakage. Noise-aware Bayesian inference framework for locally private data collection.
problem Privacy-preserving data collection with non-trustworthy aggregators.
method Noise-aware probabilistic modeling framework for Bayesian inference under LDP.
result Demonstrated efficacy in parameter estimation for various distributions and regression models.
This paper develops embeddings that preserve likelihood-based statistical inference.
problem Modern machine learning embeddings destroy the geometric structure required for likelihood-based inference.
method Developed a rigorous theory of likelihood-preserving embeddings and introduced the Likelihood-Ratio Distortion metric.
result Controlling the distortion Δn is necessary and sufficient for preserving inference. Novel algorithm reduces privacy noise in machine learning.
problem High privacy noise in machine learning algorithms.
method Robust statistics, specifically median and trimmed mean, to bound sensitivity of SGD iterates.
result Improved privacy-utility trade-off with reduced noise and computational efficiency.
A new privacy-preserving mechanism for shapes on manifolds.
problem Privacy-preserving sanitization of shapes on curved manifolds.
method Developed a K-norm gradient mechanism on Riemannian manifolds.
result The K-norm gradient mechanism offers better control over sensitivity than the Laplace mechanism on positively curved manifolds.
Framework for AI customer support that protects privacy and reduces costs.
problem Privacy risks and compliance challenges in AI customer support.
method Zero-shot learning with large language models, real-time data anonymization, retrieval-augmented generation, robust post-processing.
result Reduces privacy risks and compliance costs while maintaining accuracy.
ARF synthesizes epidemiological data to match original findings.
problem Synthetic data quality and privacy in epidemiology.
method Adversarial Random Forests (ARF) for efficient data synthesis.
result ARF-generated synthetic data consistently matches original epidemiological findings.
Generative adversarial networks (GAN) have been effective for learning generative models for real-world data. However, existing GANs (GAN and its variants) tend to suffer from training problems such as instability and mode collapse. In this paper, we propose a novel GAN framework called evolutionary generative adversar…
Tempered sigmoids improve deep learning privacy.
problem Privacy-preserving deep learning with strict differential privacy guarantees.
method Developed tempered sigmoid activation functions for deep learning models.
result Tempered sigmoids outperform ReLU in achieving state-of-the-art accuracy.
A privacy-preserving algorithm for high-dimensional bandits.
problem High-dimensional stochastic contextual linear bandits with sparse parameters under privacy constraints.
method PrivateLASSO algorithm based on sparse hard-thresholding and episodic thresholding.
result Minimax private lower bounds and utility guarantees for PrivateLASSO.
We propose a novel and flexible rank-breaking-then-composite-marginal-likelihood (RBCML) framework for learning random utility models (RUMs), which include the Plackett-Luce model. We characterize conditions for the objective function of RBCML to be strictly log-concave by proving that strict log-concavity is preserved…