Proposes using probabilistic models for privacy-preserving synthetic data.
problem Designing high-quality synthetic data for privacy preservation.
method Formulate the problem through probabilistic modelling, choosing a model for the data.
result Statistical discoveries can be reliably reproduced from synthetic data.
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.
DP-FedTabDiff generates private synthetic tabular data using diffusion models and differential privacy.
problem Privacy-preserving synthetic data generation for tabular data in regulated domains.
method Combines Differential Privacy, Federated Learning, and Denoising Diffusion Probabilistic Models.
result Achieves significant privacy improvements without compromising data quality.
Proposes differentially private normalizing flows for privacy-preserving density estimation.
problem Privacy concerns in density estimation models when individuals are directly associated with the training data.
method Uses normalizing flow models with explicit differential privacy guarantees.
result Substantially outperforms previous state-of-the-art approaches in privacy-preserving density estimation.
We propose a data-driven framework for optimizing privacy-preserving data release mechanisms to attain the information-theoretically optimal tradeoff between minimizing distortion of useful data and concealing specific sensitive information. Our approach employs adversarially-trained neural networks to implement random…
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.
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.
Proposes DP-MERF for privacy-preserving synthetic data generation.
problem Privacy-preserving data generation for synthetic datasets.
method Differentially private mean embeddings with random features.
result Achieves better privacy-utility trade-offs than existing methods.
A framework for privacy-preserving DNN pruning and acceleration.
problem Privacy concerns in DNN weight pruning for mobile devices.
method ADMM-based iterative pruning with synthetic data, compiler optimizations.
result 4.2X, 2.5X, and 2.0X speedup with almost no accuracy loss.
The use of inverse probability weighting (IPW) methods to estimate the causal effect of treatments from observational studies is widespread in econometrics, medicine and social sciences. Although these studies often involve sensitive information, thus far there has been no work on privacy-preserving IPW methods. We add…
Diffusion models generate private synthetic images with high quality.
problem Generating private synthetic images from sensitive datasets.
method Differentially private fine-tuning of pre-trained diffusion models.
result Improved synthetic data quality and accuracy compared to non-private models.
Framework audits synthetic datasets for trustworthiness across various use cases.
problem Assessing the trustworthiness of synthetic datasets and models.
method Holistic auditing framework focusing on bias, fidelity, utility, robustness, and privacy.
result Introduces a trustworthiness index and model selection process for controllable trade-offs.
Eye tracking is handled as one of the key technologies for applications that assess and evaluate human attention, behavior, and biometrics, especially using gaze, pupillary, and blink behaviors. One of the challenges with regard to the social acceptance of eye tracking technology is however the preserving of sensitive …
A privacy-preserving synthetic data generation framework that distinguishes between true and phantom data disclosures.
problem Detecting and explaining data disclosures in synthetic datasets.
method Customizable empirical auditing framework with statistical hypothesis testing.
result Demonstrated tighter privacy leakage bounds than prior methods.
FedSyn generates synthetic data from multiple organizations' datasets.
problem Generating diverse synthetic data from limited datasets.
method Federated learning and GAN for privacy-preserving synthetic data generation.
result Synthetic data can be generated from diverse datasets without accessing individual data.
Latent Noise Injection improves synthetic data generation for privacy and statistical alignment.
problem Slow convergence of generative models in high-dimensional settings.
method Latent Noise Injection using Masked Autoregressive Flows (MAF).
result Synthetic data closely reflects the underlying distribution, especially in high-dimensional settings.
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.
New protocol evaluates synthetic data for temporal consistency.
problem Synthetic data generators can produce invalid timestamps and trajectories.
method Characterize datasets by four properties, then measure timestamp validity and dynamics.
result Temporal fidelity must be measured, not inferred from static data.
Contextual bandit algorithms~(CBAs) often rely on personal data to provide recommendations. Centralized CBA agents utilize potentially sensitive data from recent interactions to provide personalization to end-users. Keeping the sensitive data locally, by running a local agent on the user's device, protects the user's p…
Paper presents a privacy-preserving algorithm for estimating peer effects using the Ising model.
problem Privacy concerns in estimating peer effects using network data.
method Developed a (ε,δ)-differentially private algorithm using Ising model. result Established regret bounds and validated performance on synthetic and real-world networks.
The paper develops methods to create private synthetic spatial point patterns.
problem Generating private synthetic spatial point patterns.
method Developed differentially private Poisson and Cox point synthesizers.
result The synthesizers effectively maintain privacy and utility of synthetic data.
Synthetic data improves credit scoring models' performance without compromising borrower privacy.
problem Scarcity of real data for credit scoring models due to privacy concerns.
method Privacy-preserving training with synthetic data.
result Credit scoring models trained with synthetic data show a reduction of 3% in AUC and 6% in KS compared to real data models.
Privacy-preserving reinforcement learning from human feedback using decoupled reward modeling.
problem Training large language models with sensitive user information while preserving privacy.
method Proposes a privacy-preserving framework that imposes differential privacy on reward learning only.
result Privacy contributes an additional additive term to the suboptimality gap, and the upper bound is rate-optimal up to logarithmic factors.
New defense method protects client data privacy in federated learning.
problem Gradient leakage attacks in federated learning.
method Learning to obscure data to generate synthetic samples.
result Synthetic samples preserve predictive features and protect privacy.
Paper proposes a privacy-preserving RL algorithm for linear MDPs with theoretical guarantees.
problem Protecting users' private data in personalized services using RL.
method Local differential privacy (LDP) for RL with linear function approximation.
result Achieves a regret bound of $O(d^{5/4}H^{7/4}T^{3/4}\left(\log(1/δ)
ight)^{1/4}\sqrt{1/\varepsilon})$ for linear mixture MDPs.
DP-CGAN generates private synthetic data and labels.
problem Preserving privacy in synthetic data generation.
method Differentially private conditional GAN (DP-CGAN) with clipping and perturbation.
result DP-CGAN generates visually and empirically promising results on MNIST with low privacy cost.
Method preserves correlations in synthetic data.
problem Preserving dependence structure of original data.
method Orthogonal Procrustes problem for restoring Pearson correlation.
result Restores Pearson correlation structure while preserving feature distributions and downstream tasks performance.
Framework evaluates quality of synthetic data generated with differential privacy.
problem Ensuring synthetic data retains statistical quality after applying differential privacy.
method Developed a framework to evaluate synthetic data quality from a practical researcher's viewpoint.
result Synthetic data can be evaluated against training data or underlying populations, and for specific tasks like inference or prediction.
DP-CDA generates synthetic data to enhance privacy in high-dimensional datasets.
problem Privacy concerns in anonymized datasets, especially in high-dimensional data.
method Randomized mixing of privacy-sensitive data in a class-specific manner with carefully tuned randomness.
result DP-CDA provides stronger privacy guarantees compared to existing methods, maintaining utility.
Tensor factorization has been demonstrated as an efficient approach for computational phenotyping, where massive electronic health records (EHRs) are converted to concise and meaningful clinical concepts. While distributing the tensor factorization tasks to local sites can avoid direct data sharing, it still requires t…
FL-Sailer enables federated learning for scATAC-seq data, reducing dimensionality and noise.
problem Privacy-preserving federated learning for ultra-high dimensional, sparse, and heterogeneous scATAC-seq data.
method FL-Sailer integrates adaptive leverage score sampling and an invariant VAE architecture.
result FL-Sailer converges to an approximate solution with bounded error, surpassing centralized methods.
In this paper, we address the problem of data reconstruction from privacy-protected templates, based on recent concept of sparse ternary coding with ambiguization (STCA). The STCA is a generalization of randomization techniques which includes random projections, lossy quantization, and addition of ambiguization noise t…
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.
DP-NCB algorithm ensures privacy and fairness in bandit decisions.
problem Achieving both privacy and fairness in bandit algorithms.
method Differentially Private Nash Confidence Bound (DP-NCB) framework.
result DP-NCB achieves optimal Nash regret while maintaining privacy.
FinDiff generates synthetic financial data for regulatory tasks.
problem Sharing microdata for research due to privacy regulations.
method Diffusion model using embedding encodings for mixed modality financial data.
result FinDiff excels in generating high-fidelity, privacy-preserving synthetic financial data.
This paper uses deep generative models to create synthetic financial data for portfolio and risk modeling.
problem Challenges in empirical research due to privacy, accessibility, and reproducibility issues in financial data.
method Investigates the use of Time-series Generative Adversarial Networks (TimeGAN) and Variational Autoencoders (VAEs) to generate synthetic financial return series.
result Synthetic data from TimeGAN closely mimics real financial data in distributional shapes, volatility, and autocorrelation.
Paper tackles privacy-preserving data density issues using deconvolution.
problem Privacy-preserving noise affects data density, leading to under/over-estimation.
method Develops deconvoluting kernel density estimators and regression models.
result Demonstrates improved accuracy in estimating heavy-hitters with locally differential data.
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.
CDA framework infers channel influence from aggregated data without user identifiers.
problem Lack of user-level path data due to privacy regulations and platform restrictions.
method CDA integrates PCMCI for causal discovery and Structural Causal Model for effect estimation.
result CDA achieves strong accuracy in estimating channel influence, even under structural uncertainty.
This paper analyzes privacy-preserving methods for collaborative forecasting.
problem Data owners' reluctance to share data due to competitive and privacy concerns.
method Examines three groups of privacy-preserving methods: data transformation, secure multi-party computations, and decomposition methods.
result State-of-the-art techniques have limitations in preserving data privacy, such as trade-offs between privacy and forecasting accuracy.
Generative Adversarial Network (GAN) and its variants serve as a perfect representation of the data generation model, providing researchers with a large amount of high-quality generated data. They illustrate a promising direction for research with limited data availability. When GAN learns the semantic-rich data distri…
A new algorithm improves federated learning by combining knowledge distillation and weighted combination loss.
problem Non-IID client data in federated learning leads to model drift and poor generalization.
method pFedKD-WCL integrates knowledge distillation with bi-level optimization to address non-IID challenges.
result pFedKD-WCL outperforms state-of-the-art algorithms in accuracy and convergence speed.
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.
Proposes a transfer learning framework for sparse SIMs without raw source data.
problem Lack of direct access to raw source data and known link functions in transfer learning.
method Source-data-free framework based on SIM, using summary statistics and a multilayer perceptron.
result Consistent improvements over existing approaches in synthetic and real-world data.
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.
Differentially private method for synthetic data generation from vertically partitioned data.
problem Generating synthetic data from vertically partitioned data while preserving privacy.
method Differentially private stochastic gradient descent (DP-SGD) algorithm combined with secure multiparty computation (MPC).
result Comparable accuracy to non-partitioned data, demonstrating privacy-preserving synthetic data generation.
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.
The Internet of Things (IoT) will be a main data generation infrastructure for achieving better system intelligence. However, the extensive data collection and processing in IoT also engender various privacy concerns. This paper provides a taxonomy of the existing privacy-preserving machine learning approaches develope…