New method protects group privacy in sampling, overcoming traditional privacy limits.
problem Protecting group privacy in statistical sampling with strong differential privacy guarantees.
method Integrates boosting theory for non-private density estimation and bypasses sensitivity analysis.
result Achieves strong privacy guarantees for sampling without scaling noise variance.
P3LS preserves privacy while integrating data across companies.
problem Privacy concerns in cross-organizational data exchange and integration.
method Privacy-preserving federated learning technique using SVD-based PLS and random masks.
result Improves prediction performance on process-related indicators.
Integrates differential privacy and demographic parity in multi-class classification.
problem Ensuring fairness and privacy in sensitive applications.
method Designs DP2DP algorithm that enforces both demographic parity and differential privacy.
result DP2DP converges towards demographic parity at nearly the same rate as non-private methods, achieving state-of-the-art trade-offs.
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.
Paper proposes a method to numerically approximate differential privacy guarantees using FFT.
problem Computing tight differential privacy guarantees for machine learning algorithms.
method The method uses numerical approximation of an integral formula, discretization, and the fast Fourier transform algorithm.
result Significant improvements in bound tightness and computation time compared to state-of-the-art techniques.
DPNR preserves privacy of text representations using differential privacy.
problem Privacy leakage in deep learning text representations.
method DPNR uses Differential Privacy to provide formal privacy guarantees and dropout masking for enhanced privacy.
result DPNR reduces privacy leakage without significantly sacrificing main task performance.
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.
Framework evaluates privacy cost of non-private pre-processing in DP pipelines.
problem Privacy cost of non-private data-dependent pre-processing in DP machine learning pipelines.
method Establishes upper bounds on overall privacy guarantees using Smooth DP and bounded sensitivity.
result Explicit overall privacy guarantees for various pre-processing algorithms.
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.
Efficiently calculates privacy guarantees for 2020 Census data.
problem Evaluate privacy guarantees for 2020 U.S. Census data releases.
method Sieve-accelerated quadrature method to evaluate tail probabilities of high-dimensional convolutions.
result Achieves 1,824-fold speedup over prior methods while maintaining error tolerances.
NKI integrates obfuscated datasets using nonlinear kernels for improved data collaboration.
problem Privacy-preserving data collaboration with reduced reconstruction risk.
method Formulates linear kernel integration, kernelizes it, and introduces graph regularization and centering constraints.
result NKI improves classification accuracy over existing linear integration methods under nonlinear dimensionality reduction.
Paper simplifies DP composition for adaptive privacy budgets, enabling better privacy and accuracy in deep learning.
problem Tension between efficiency and flexibility in DP composition theorems.
method Rényi Differential Privacy (RDP) for adaptive privacy budgets, proving simpler composition theorem with smaller constants.
result Practical DP composition for adaptive privacy budgets, enabling better privacy and accuracy in deep learning.
New privacy method for eye tracking data reduces correlations and maintains accuracy.
problem Privacy concerns in eye tracking data from VR/AR glasses.
method Transform-coding based differential privacy mechanism for eye movement data.
result Significant reductions in sample correlations and query sensitivities, providing high privacy without loss in accuracy.
Paper relaxes differential privacy for correlated features, improving privacy-utility trade-off.
problem Standard differential privacy ignores feature correlation, leading to suboptimal privacy-utility balance.
method Introduces CorrDP framework that accounts for feature correlation, using total variation distance for quantification.
result CorrDP algorithms outperform standard DP in synthetic and real-world datasets with insensitive features.
A new federated learning framework with sparsification and adaptive optimization for privacy and efficiency.
problem Lack of sufficient privacy protection in federated learning.
method Integrates random sparsification with gradient perturbation and acceleration techniques to enhance privacy and efficiency.
result Outperforms previous differentially-private federated learning approaches in privacy and efficiency.
Enhanced privacy, utility, and efficiency through MUST subsampling.
problem Balancing privacy, utility, and computational efficiency in data analysis.
method MUltistage Sampling Technique (MUST) for privacy amplification in differential privacy.
result MUST offers stronger privacy guarantees (ϵ) than one-stage subsampling methods while maintaining similar utility and computational efficiency. Enhances privacy-preserving logistic regression for diabetes prediction.
problem Maintaining privacy while improving prediction accuracy in machine learning.
method Proposes stacking to enhance privacy-preserving logistic regression, either sample-based or feature-based.
result Feature-based partitioning requires fewer samples than sample-based, potentially offering better performance.
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.
Synth-MIA assesses privacy leakage in synthetic tabular data models.
problem Challenges in evaluating privacy leakage in synthetic tabular data.
method Unified threat framework deploying multiple attacks.
result Higher synthetic data quality correlates with greater privacy leakage.
Paper introduces ITD for detecting distributional changes in decentralized learning environments.
problem Detecting distributional changes in decentralized learning environments with data privacy and heterogeneity concerns.
method Introduces Integrated Transportation Distance (ITD) for two-sample testing in federated learning.
result ITD effectively aggregates information across distributed clients, detecting subtle distributional shifts.
Dp-CLIP preserves privacy in multimodal AI training.
problem Privacy concerns in multimodal AI, especially in vision-language tasks.
method Differentially private adaptation of CLIP model.
result Dp-CLIP retains accuracy while ensuring privacy.
Federated learning improves CRC grading accuracy and privacy.
problem Inter-observer variability and data privacy in CRC grading.
method Multi-scale federated learning framework integrating ResNetRS50.
result Framework achieves 83.5% accuracy, outperforming centralized models.
Paper tightens privacy and generalization bounds for iterative learning.
problem Balancing privacy and generalization in iterative learning algorithms.
method Established alignment between generalization and privacy, derived composition theorems for iterative algorithms.
result Generalization bounds for iterative learning algorithms are strictly tighter than existing works.
A privacy-preserving framework detects faults in circular economy processes.
problem Lack of shared data across company borders due to privacy concerns.
method Federated Principal Component Analysis (PCA) and Secure Multiparty Computation.
result The proposed FedMSPC framework outperforms standard PCA in fault detection.
Normalization layers improve the accuracy of Differentially Private training of deep neural networks.
problem Reduced accuracy in deep neural networks with Differentially Private training.
method Proposed a novel method for integrating batch normalization with Differentially Private Stochastic Gradient Descent (DPSGD) without additional privacy loss.
result Training deeper networks with better utility-privacy trade-off is possible.
New framework provides privacy guarantees for practical federated learning.
problem Inadequate privacy guarantees for federated learning due to restrictive assumptions.
method Fed-α-NormEC, integrating multiple local updates, partial client participation, and standard assumptions. result Provably convergent and differentially private federated learning framework.
Paper develops privacy-preserving mechanisms for machine learning using wavelet transforms.
problem Improper data privacy methods compromise user data even with small preliminary knowledge.
method Three privacy-preserving mechanisms with discrete M-band wavelet transform.
result Successfully retains both differential privacy and learnability in various machine learning environments.
edPLS adds Gaussian noise to PLS regression to protect data privacy.
problem Protecting sensitive data in PLS regression models.
method Integrates Gaussian noise into PLS algorithm based on global sensitivity.
result Effective at preserving privacy while maintaining competitive prediction accuracy.
PrAda-GAN improves synthetic data generation under differential privacy.
problem Generating synthetic data under differential privacy with marginal-based methods.
method Sequential generator architecture integrating GAN and marginal-based approaches, with adaptive regularization of Bayes network structure.
result PrAda-GAN outperforms existing methods in privacy-utility trade-off on synthetic and real-world datasets.
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.
This paper reviews security and privacy issues in deep learning models.
problem Security and privacy threats in deep learning models.
method Analysis of security and privacy attacks, including poisoning and evasion attacks, and proposed defenses. Also, review of data privacy threats and solutions.
result Various security and privacy attacks and their defenses are discussed.
We extend Gaussian Differential Privacy to curved Riemannian manifolds.
problem Extending Gaussian Differential Privacy to curved spaces.
method Developed a Riemannian Gaussian distribution using the Bishop-Gromov theorem and a MCMC-based algorithm.
result Achieved Gaussian Differential Privacy on general Riemannian manifolds with bounded Ricci curvature.
Develops privacy-preserving multivariate median estimation methods.
problem Lack of rigorous privacy guarantees for robust multivariate location estimation.
method Novel finite-sample performance guarantees for differentially private multivariate depth-based medians.
result Sharp performance guarantees for multivariate depth-based medians under differential privacy.
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.
This paper presents a method to train a public model with private data using GANs and differential privacy.
problem Privacy concerns in training deep learning models on sensitive data.
method A three-player learning framework with differential privacy protection.
result The proposed method achieves a balance between privacy and model accuracy.
Federated Forest tackles privacy-preserving machine learning for scattered data.
problem Privacy and data integration challenges for AI applications.
method Proposes Federated Forest, a privacy-preserving machine learning model based on random forest.
result Achieves the same accuracy as non-privacy-preserving models without data exchange.
Federated ABC-GMM boosts classification performance in imbalanced clinical data.
problem Class imbalance and privacy concerns in federated learning for clinical data.
method Approximate Bayesian computation-based Gaussian Mixture Model (Federated ABC-GMM) for privacy-preserving oversampling.
result Federated ABC-GMM improves F1 score up to nearly ideal performance in imbalanced clinical data.
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.
Big data transforms accounting and auditing, enhancing insights but posing challenges.
problem Challenges in data privacy and security with increased data sources.
method Utilizing AI and machine learning for efficient data analysis and anomaly detection.
result Enhanced analytics tools and continuous learning are key to overcoming challenges.
Paper introduces efficient top-k selection with differential privacy.
problem Efficiently selecting top-k elements with differential privacy.
method Oneshot Laplace mechanism, generalizing Report Noisy Max.
result Noise level of O(sqrt(k)/eps) for approximate differential privacy.
FedHDPrivacy uses DP to improve FL in IoT, maintaining high accuracy.
problem Privacy threats in FL, especially in IoT environments.
method Integrates DP with neuro-symbolic computing, actively monitoring and adjusting noise.
result Maintains high performance in manufacturing monitoring, surpassing other FL methods.
This paper explores how LLMs can improve pipeline-based conversational agents.
problem Limitations of pipeline-based conversational agents in human-like conversations.
method Investigated LLMs' capabilities in two phases: design and development, and operations.
result LLMs can enhance pipeline-based agents in various tasks like data generation, intent classification, and auto-correction.
Improved fraud detection in finance with quantum-enhanced federated learning.
problem Challenges in detecting financial fraud with traditional methods.
method Hybrid quantum-enhanced federated learning framework combining quantum LSTM with privacy-preserving techniques.
result Approximately 5% improvement in performance metrics compared to conventional models.
APGE protects graph node representations from inference attacks.
problem Privacy leakage in graph embedding methods.
method Adversarial training framework with disentangling and purging mechanisms.
result APGE preserves structural and utility attributes while concealing private information.
DP-LSTM predicts stock prices using financial news with improved accuracy and privacy.
problem Predicting stock prices with financial news articles.
method Integrates financial news articles into a sentiment-ARMA model, then uses an LSTM network with differential privacy.
result Achieves up to 65.79% improvement in MSE for S&P 500 prediction.
While significant progress has been made separately on analytics systems for scalable stochastic gradient descent (SGD) and private SGD, none of the major scalable analytics frameworks have incorporated differentially private SGD. There are two inter-related issues for this disconnect between research and practice: (1)…
Proposes a method to generate private synthetic data in a decentralized setting using correlated noise.
problem Challenges of generating private synthetic data in a decentralized setting with limited client data.
method Integrates CAPE protocol into federated DP-CDA framework to generate anti-correlated noise.
result Improves privacy-utility trade-off in federated setting compared to centralized approach.
Secure transfer learning framework improves model flexibility without compromising privacy.
problem Scattered data across organizations limits machine learning.
method Federated Transfer Learning (FTL) framework with secure transfer cross validation.
result Models can be built more flexibly and accurately with shared labels from different sources.