{"total":78,"rows":[{"id":"2206.00240","title":"Privacy for Free: How does Dataset Condensation Help Privacy?","created":"2022-06-01","cited_by_count":120,"area":null},{"id":"2302.04062","title":"Machine Learning for Synthetic Data Generation: A Review","created":"2023-02-08","cited_by_count":120,"area":null},{"id":"2210.09929","title":"Differentially Private Diffusion Models","created":"2022-10-18","cited_by_count":119,"area":"diffusion"},{"id":"2301.04272","title":"Data Distillation: A Survey","created":"2023-01-11","cited_by_count":87,"area":null},{"id":"2302.13861","title":"Differentially Private Diffusion Models Generate Useful Synthetic Images","created":"2023-02-27","cited_by_count":86,"area":"diffusion"},{"id":"2305.15560","title":"Differentially Private Synthetic Data via Foundation Model APIs 1: Images","created":"2023-05-24","cited_by_count":65,"area":null},{"id":"2307.02106","title":"SoK: Privacy-Preserving Data Synthesis","created":"2023-07-05","cited_by_count":38,"area":null},{"id":"2209.07400","title":"Private Synthetic Data for Multitask Learning and Marginal Queries","created":"2022-09-15","cited_by_count":35,"area":null},{"id":"2402.13659","title":"Privacy-Preserving Instructions for Aligning Large Language Models","created":"2024-02-21","cited_by_count":35,"area":"alignment"},{"id":"2211.04446","title":"Private Set Generation with Discriminative Information","created":"2022-11-07","cited_by_count":34,"area":null},{"id":"2305.15759","title":"DP-LDMs: Differentially Private Latent Diffusion Models","created":"2023-05-25","cited_by_count":29,"area":"diffusion"},{"id":"2205.12900","title":"Pre-trained Perceptual Features Improve Differentially Private Image Generation","created":"2022-05-25","cited_by_count":28,"area":null},{"id":"2106.04590","title":"PEARL: Data Synthesis via Private Embeddings and Adversarial Reconstruction Learning","created":"2021-06-08","cited_by_count":23,"area":null},{"id":"2106.05042","title":"Hermite Polynomial Features for Private Data Generation","created":"2021-06-09","cited_by_count":21,"area":null},{"id":"2406.01355","title":"Differentially Private Fine-Tuning of Diffusion Models","created":"2024-06-03","cited_by_count":20,"area":"diffusion"},{"id":"2306.03257","title":"Generating Private Synthetic Data with Genetic Algorithms","created":"2023-06-05","cited_by_count":18,"area":null},{"id":"2302.02936","title":"Private GANs, Revisited","created":"2023-02-06","cited_by_count":17,"area":null},{"id":"2303.01687","title":"Differentially Private Neural Tangent Kernels for Privacy-Preserving Data Generation","created":"2023-03-03","cited_by_count":15,"area":null},{"id":"2503.05954","title":"A Survey on Deep Learning Approaches for Tabular Data Generation: Utility, Alignment, Fidelity, Privacy, Diversity, and Beyond","created":"2025-03-07","cited_by_count":15,"area":null},{"id":"2211.11591","title":"DPD-fVAE: Synthetic Data Generation Using Federated Variational Autoencoders With Differentially-Private Decoder","created":"2022-11-21","cited_by_count":14,"area":null},{"id":"2208.03409","title":"DP$^2$-VAE: Differentially Private Pre-trained Variational Autoencoders","created":"2022-08-05","cited_by_count":13,"area":null},{"id":"2307.10430","title":"DP-TBART: A Transformer-based Autoregressive Model for Differentially Private Tabular Data Generation","created":"2023-07-19","cited_by_count":13,"area":null},{"id":"2311.12850","title":"PrivImage: Differentially Private Synthetic Image Generation using Diffusion Models with Semantic-Aware Pretraining","created":"2023-10-19","cited_by_count":13,"area":"diffusion"},{"id":"2502.05505","title":"Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model","created":"2025-02-08","cited_by_count":13,"area":"synth"},{"id":"2503.14681","title":"DPImageBench: A Unified Benchmark for Differentially Private Image Synthesis","created":"2025-03-18","cited_by_count":11,"area":null},{"id":"2205.14485","title":"Noise-Aware Statistical Inference with Differentially Private Synthetic Data","created":"2022-05-28","cited_by_count":9,"area":null},{"id":"2506.06108","title":"Synthetic Tabular Data: Methods, Attacks and Defenses","created":"2025-06-06","cited_by_count":9,"area":null},{"id":"2211.15809","title":"On the Utility Recovery Incapability of Neural Net-based Differential Private Tabular Training Data Synthesizer under Privacy Deregulation","created":"2022-11-28","cited_by_count":8,"area":null},{"id":"2504.01395","title":"From Easy to Hard: Building a Shortcut for Differentially Private Image Synthesis","created":"2025-04-02","cited_by_count":7,"area":"diffusion"},{"id":"2411.03351","title":"Tabular Data Synthesis with Differential Privacy: A Survey","created":"2024-11-04","cited_by_count":6,"area":null},{"id":"2305.17148","title":"Differentially Private Low-dimensional Synthetic Data from High-dimensional Datasets","created":"2023-05-26","cited_by_count":4,"area":null},{"id":"2309.15696","title":"A Unified View of Differentially Private Deep Generative Modeling","created":"2023-09-27","cited_by_count":4,"area":null},{"id":"2312.08227","title":"Differentially Private Gradient Flow based on the Sliced Wasserstein Distance","created":"2023-12-13","cited_by_count":4,"area":null},{"id":"2409.02404","title":"Learning Privacy-Preserving Student Networks via Discriminative-Generative Distillation","created":"2024-09-04","cited_by_count":4,"area":null},{"id":"2503.20846","title":"Generating Synthetic Data with Formal Privacy Guarantees: State of the Art and the Road Ahead","created":"2025-03-26","cited_by_count":4,"area":null},{"id":"2508.01749","title":"Improving Noise Efficiency in Privacy-preserving Dataset Distillation","created":"2025-08-03","cited_by_count":4,"area":null},{"id":"2410.02246","title":"PFGuard: A Generative Framework with Privacy and Fairness Safeguards","created":"2024-10-03","cited_by_count":3,"area":null},{"id":"2502.12794","title":"RAPID: Retrieval Augmented Training of Differentially Private Diffusion Models","created":"2025-02-18","cited_by_count":3,"area":"rag"},{"id":"2202.06467","title":"NeuroMixGDP: A Neural Collapse-Inspired Random Mixup for Private Data Release","created":"2022-02-14","cited_by_count":2,"area":null},{"id":"2309.04427","title":"Robust Representation Learning for Privacy-Preserving Machine Learning: A Multi-Objective Autoencoder Approach","created":"2023-09-08","cited_by_count":2,"area":null},{"id":"2402.03985","title":"A Bias-Variance Decomposition for Ensembles over Multiple Synthetic Datasets","created":"2024-02-06","cited_by_count":2,"area":null},{"id":"2406.05257","title":"Efficient Differentially Private Fine-Tuning of Diffusion Models","created":"2024-06-07","cited_by_count":2,"area":"diffusion"},{"id":"2408.14738","title":"Learning Differentially Private Diffusion Models via Stochastic Adversarial Distillation","created":"2024-08-27","cited_by_count":2,"area":"diffusion"},{"id":"2410.19941","title":"Privacy without Noisy Gradients: Slicing Mechanism for Generative Model Training","created":"2024-10-25","cited_by_count":2,"area":null},{"id":"2502.01701","title":"Learning with Differentially Private (Sliced) Wasserstein Gradients","created":"2025-02-03","cited_by_count":2,"area":null},{"id":"2502.13193","title":"Private Text Generation by Seeding Large Language Model Prompts","created":"2025-02-18","cited_by_count":2,"area":null},{"id":"2212.11468","title":"IPProtect: protecting the intellectual property of visual datasets during data valuation","created":"2022-12-22","cited_by_count":1,"area":null},{"id":"2301.13389","title":"Differentially Private Kernel Inducing Points using features from ScatterNets (DP-KIP-ScatterNet) for Privacy Preserving Data Distillation","created":"2023-01-31","cited_by_count":1,"area":null},{"id":"2304.05195","title":"HPN: Personalized Federated Hyperparameter Optimization","created":"2023-04-11","cited_by_count":1,"area":null},{"id":"2306.13211","title":"Differentially Private Synthetic Data Using KD-Trees","created":"2023-06-19","cited_by_count":1,"area":null},{"id":"2308.04755","title":"Collaborative Learning From Distributed Data With Differentially Private Synthetic Twin Data","created":"2023-08-09","cited_by_count":1,"area":null},{"id":"2309.12380","title":"Methods for generating and evaluating synthetic longitudinal patient data: a systematic review","created":"2023-09-21","cited_by_count":1,"area":null},{"id":"2312.01201","title":"PAC Privacy Preserving Diffusion Models","created":"2023-12-02","cited_by_count":1,"area":"diffusion"},{"id":"2411.16121","title":"DP-CDA: An Algorithm for Enhanced Privacy Preservation in Dataset Synthesis Through Randomized Mixing","created":"2024-11-25","cited_by_count":1,"area":null},{"id":"2506.19360","title":"SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation","created":"2025-06-24","cited_by_count":1,"area":null},{"id":"2509.10367","title":"A Discrepancy-Based Perspective on Dataset Condensation","created":"2025-09-12","cited_by_count":1,"area":null},{"id":"2511.07997","title":"PrAda-GAN: A Private Adaptive Generative Adversarial Network with Bayes Network Structure","created":"2025-11-11","cited_by_count":1,"area":null},{"id":"2110.03017","title":"Two-Bit Aggregation for Communication Efficient and Differentially Private Federated Learning","created":"2021-10-06","cited_by_count":0,"area":null},{"id":"2211.14062","title":"M$^2$M: A general method to perform various data analysis tasks from a differentially private sketch","created":"2022-11-25","cited_by_count":0,"area":null},{"id":"2304.12185","title":"DPAF: Image Synthesis via Differentially Private Aggregation in Forward Phase","created":"2023-04-20","cited_by_count":0,"area":null},{"id":"2305.10662","title":"Private Gradient Estimation is Useful for Generative Modeling","created":"2023-05-18","cited_by_count":0,"area":null},{"id":"2309.00008","title":"Large-Scale Public Data Improves Differentially Private Image Generation Quality","created":"2023-08-04","cited_by_count":0,"area":null},{"id":"2310.04528","title":"DPGOMI: Differentially Private Data Publishing with Gaussian Optimized Model Inversion","created":"2023-10-06","cited_by_count":0,"area":null},{"id":"2406.13947","title":"AspirinSum: an Aspect-based utility-preserved de-identification Summarization framework","created":"2024-06-20","cited_by_count":0,"area":null},{"id":"2408.08399","title":"Transformer-based few-shot learning for modeling Electricity Consumption Profiles with minimal data across thousands of domains","created":"2024-08-15","cited_by_count":0,"area":null},{"id":"2409.12384","title":"Privacy-Preserving Student Learning with Differentially Private Data-Free Distillation","created":"2024-09-19","cited_by_count":0,"area":null},{"id":"2409.18611","title":"Differentially Private Non Parametric Copulas: Generating synthetic data with non parametric copulas under privacy guarantees","created":"2024-09-27","cited_by_count":0,"area":null},{"id":"2503.16195","title":"VP-NTK: Exploring the Benefits of Visual Prompting in Differentially Private Data Synthesis","created":"2025-03-20","cited_by_count":0,"area":null},{"id":"2511.13893","title":"Beyond One-Size-Fits-All: Neural Networks for Differentially Private Tabular Data Synthesis","created":"2025-11-17","cited_by_count":0,"area":null},{"id":"2601.06368","title":"From Easy to Hard++: Promoting Differentially Private Image Synthesis Through Spatial-Frequency Curriculum","created":"2026-01-10","cited_by_count":0,"area":null},{"id":"2601.19090","title":"Privacy-Preserving Model Transcription with Differentially Private Synthetic Distillation","created":"2026-01-27","cited_by_count":0,"area":null},{"id":"2604.05256","title":"Protecting and Preserving Protest Dynamics for Responsible Analysis","created":"2026-04-06","cited_by_count":0,"area":null},{"id":"2606.16411","title":"Not all Jensen-Shannon Divergence Estimators are Equal","created":"2026-06-15","cited_by_count":0,"area":null},{"id":"2606.21240","title":"DIPBox: A Multi-scale Testing Framework for Tracking Dataset Regeneration","created":"2026-06-19","cited_by_count":0,"area":null},{"id":"2607.07565","title":"Collaborative Synthetic Data Generation for Knowledge Transfer in Federated Learning","created":"2026-07-08","cited_by_count":0,"area":null},{"id":"2607.29100","title":"StraightDP: Geometry-Aware Differential Privacy for Rectified-Flow Transformers","created":"2026-07-31","cited_by_count":0,"area":null},{"id":"2609.00708","title":"Differentially Private Paired Table-Image Multimodal Synthesis","created":"2026-09-01","cited_by_count":0,"area":null},{"id":"2609.05253","title":"GLASS: Graph-Language Alignment with Spherical Scoring for Transferable Graph-Level Anomaly Detection","created":"2026-09-04","cited_by_count":0,"area":null}],"next_cursor":null}