{"total":91,"rows":[{"id":"2303.00654","title":"How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy","created":"2023-03-01","cited_by_count":153,"area":null},{"id":"2210.14348","title":"Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe","created":"2022-10-25","cited_by_count":108,"area":null},{"id":"2205.03257","title":"Synthetic Data -- what, why and how?","created":"2022-05-06","cited_by_count":67,"area":null},{"id":"2109.11429","title":"Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data","created":"2021-09-23","cited_by_count":48,"area":null},{"id":"2307.02106","title":"SoK: Privacy-Preserving Data Synthesis","created":"2023-07-05","cited_by_count":38,"area":null},{"id":"2301.10053","title":"A Linear Reconstruction Approach for Attribute Inference Attacks against Synthetic Data","created":"2023-01-24","cited_by_count":24,"area":null},{"id":"2312.05114","title":"The Inadequacy of Similarity-based Privacy Metrics: Privacy Attacks against \"Truly Anonymous\" Synthetic Datasets","created":"2023-12-08","cited_by_count":14,"area":null},{"id":"2304.10819","title":"Auditing and Generating Synthetic Data with Controllable Trust Trade-offs","created":"2023-04-21","cited_by_count":13,"area":null},{"id":"2305.10994","title":"Graphical vs. Deep Generative Models: Measuring the Impact of Differentially Private Mechanisms and Budgets on Utility","created":"2023-05-18","cited_by_count":13,"area":null},{"id":"2307.03577","title":"CuTS: Customizable Tabular Synthetic Data Generation","created":"2023-07-07","cited_by_count":13,"area":null},{"id":"2310.19973","title":"Unified Enhancement of Privacy Bounds for Mixture Mechanisms via $f$-Differential Privacy","created":"2023-10-30","cited_by_count":13,"area":null},{"id":"2406.13985","title":"The Elusive Pursuit of Reproducing PATE-GAN: Benchmarking, Auditing, Debugging","created":"2024-06-20","cited_by_count":13,"area":null},{"id":"2207.05810","title":"dpart: Differentially Private Autoregressive Tabular, a General Framework for Synthetic Data Generation","created":"2022-07-12","cited_by_count":12,"area":null},{"id":"2410.05506","title":"Privacy Vulnerabilities in Marginals-based Synthetic Data","created":"2024-10-07","cited_by_count":10,"area":null},{"id":"2205.04321","title":"Evaluating the Fairness Impact of Differentially Private Synthetic Data","created":"2022-05-09","cited_by_count":9,"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":"2305.15538","title":"Post-processing Private Synthetic Data for Improving Utility on Selected Measures","created":"2023-05-24","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":"2310.19250","title":"Assessment of Differentially Private Synthetic Data for Utility and Fairness in End-to-End Machine Learning Pipelines for Tabular Data","created":"2023-10-30","cited_by_count":8,"area":null},{"id":"2401.17823","title":"Privacy-preserving data release leveraging optimal transport and particle gradient descent","created":"2024-01-31","cited_by_count":8,"area":null},{"id":"2403.07797","title":"Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data","created":"2024-03-12","cited_by_count":8,"area":null},{"id":"2405.08920","title":"Neural Collapse Meets Differential Privacy: Curious Behaviors of NoisyGD with Near-perfect Representation Learning","created":"2024-05-14","cited_by_count":8,"area":null},{"id":"2301.08844","title":"Statistical Theory of Differentially Private Marginal-based Data Synthesis Algorithms","created":"2023-01-21","cited_by_count":7,"area":null},{"id":"2109.06153","title":"Relaxed Marginal Consistency for Differentially Private Query Answering","created":"2021-09-13","cited_by_count":6,"area":null},{"id":"2210.07332","title":"Secure Multiparty Computation for Synthetic Data Generation from Distributed Data","created":"2022-10-13","cited_by_count":6,"area":null},{"id":"2306.13216","title":"Diverse Community Data for Benchmarking Data Privacy Algorithms","created":"2023-06-20","cited_by_count":6,"area":null},{"id":"2411.03351","title":"Tabular Data Synthesis with Differential Privacy: A Survey","created":"2024-11-04","cited_by_count":6,"area":null},{"id":"2202.08969","title":"Private Quantiles Estimation in the Presence of Atoms","created":"2022-02-15","cited_by_count":5,"area":null},{"id":"2306.04803","title":"Privately generating tabular data using language models","created":"2023-06-07","cited_by_count":5,"area":null},{"id":"2412.02467","title":"DP-2Stage: Adapting Language Models as Differentially Private Tabular Data Generators","created":"2024-12-03","cited_by_count":5,"area":null},{"id":"2502.06555","title":"Is API Access to LLMs Useful for Generating Private Synthetic Tabular Data?","created":"2025-02-10","cited_by_count":5,"area":null},{"id":"2504.06923","title":"The Importance of Being Discrete: Measuring the Impact of Discretization in End-to-End Differentially Private Synthetic Data","created":"2025-04-09","cited_by_count":5,"area":null},{"id":"2311.17453","title":"Privacy Measurement in Tabular Synthetic Data: State of the Art and Future Research Directions","created":"2023-11-29","cited_by_count":4,"area":null},{"id":"2312.11712","title":"A Simple and Practical Method for Reducing the Disparate Impact of Differential Privacy","created":"2023-12-18","cited_by_count":4,"area":null},{"id":"2403.13612","title":"Does Differentially Private Synthetic Data Lead to Synthetic Discoveries?","created":"2024-03-20","cited_by_count":4,"area":null},{"id":"2405.15002","title":"Private Regression via Data-Dependent Sufficient Statistic Perturbation","created":"2024-05-23","cited_by_count":4,"area":null},{"id":"2510.18768","title":"Improving the Generation and Evaluation of Synthetic Data for Downstream Medical Causal Inference","created":"2025-10-21","cited_by_count":4,"area":null},{"id":"2402.04375","title":"Bounding the Excess Risk for Linear Models Trained on Marginal-Preserving, Differentially-Private, Synthetic Data","created":"2024-02-06","cited_by_count":3,"area":null},{"id":"2506.00322","title":"dpmm: Differentially Private Marginal Models, a Library for Synthetic Tabular Data Generation","created":"2025-05-31","cited_by_count":3,"area":null},{"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":"2204.12903","title":"Spending Privacy Budget Fairly and Wisely","created":"2022-04-27","cited_by_count":2,"area":null},{"id":"2212.00135","title":"Answering Private Linear Queries Adaptively using the Common Mechanism","created":"2022-11-30","cited_by_count":2,"area":null},{"id":"2401.14577","title":"An Algorithm for Streaming Differentially Private Data","created":"2024-01-26","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":"2405.04554","title":"Differentially Private Synthetic Data with Private Density Estimation","created":"2024-05-06","cited_by_count":2,"area":null},{"id":"2405.18670","title":"Differentially Private Synthetic Data Generation for Relational Databases","created":"2024-05-29","cited_by_count":2,"area":null},{"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":"2504.00874","title":"P2NIA: Privacy-Preserving Non-Iterative Auditing","created":"2025-04-01","cited_by_count":2,"area":null},{"id":"2506.05101","title":"Privacy Amplification Through Synthetic Data: Insights from Linear Regression","created":"2025-06-05","cited_by_count":2,"area":null},{"id":"2510.03021","title":"Differentially Private Wasserstein Barycenters","created":"2025-10-03","cited_by_count":2,"area":null},{"id":"2307.04208","title":"On the Challenges of Deploying Privacy-Preserving Synthetic Data in the Enterprise","created":"2023-07-09","cited_by_count":1,"area":null},{"id":"2310.09729","title":"Private Synthetic Data Meets Ensemble Learning","created":"2023-10-15","cited_by_count":1,"area":null},{"id":"2310.20062","title":"Scalable and Privacy-Preserving Synthetic Data Generation on Decentralised Web","created":"2023-10-30","cited_by_count":1,"area":null},{"id":"2401.06273","title":"Qrlew: Rewriting SQL into Differentially Private SQL","created":"2024-01-11","cited_by_count":1,"area":null},{"id":"2407.16929","title":"Synthetic Data, Similarity-based Privacy Metrics, and Regulatory (Non-)Compliance","created":"2024-07-24","cited_by_count":1,"area":null},{"id":"2408.06997","title":"Faster Private Minimum Spanning Trees","created":"2024-08-13","cited_by_count":1,"area":null},{"id":"2410.01091","title":"Efficient and Private Marginal Reconstruction with Local Non-Negativity","created":"2024-10-01","cited_by_count":1,"area":null},{"id":"2503.02968","title":"Privacy-Preserving Fair Synthetic Tabular Data","created":"2025-03-04","cited_by_count":1,"area":null},{"id":"2504.21634","title":"Quantitative Auditing of AI Fairness with Differentially Private Synthetic Data","created":"2025-04-30","cited_by_count":1,"area":null},{"id":"2601.22434","title":"Rethinking Anonymity Claims in Synthetic Data Generation: A Model-Centric Privacy Attack Perspective","created":"2026-01-30","cited_by_count":1,"area":null},{"id":"2602.05838","title":"FHAIM: Fully Homomorphic AIM For Private Synthetic Data Generation","created":"2026-02-05","cited_by_count":1,"area":null},{"id":"2604.00868","title":"Accurate and Scalable Matrix Mechanisms via Divide and Conquer","created":"2026-04-01","cited_by_count":1,"area":null},{"id":"2604.18352","title":"Tight Auditing of Differential Privacy in MST and AIM","created":"2026-04-20","cited_by_count":1,"area":null},{"id":"2604.19653","title":"A Dual Perspective on Synthetic Trajectory Generators: Utility Framework and Privacy Vulnerabilities","created":"2026-04-21","cited_by_count":1,"area":null},{"id":"2607.07471","title":"Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data","created":"2026-07-08","cited_by_count":1,"area":null},{"id":"2111.13365","title":"Machines & Influence: An Information Systems Lens","created":"2021-11-26","cited_by_count":0,"area":null},{"id":"2310.06371","title":"Partition-based differentially private synthetic data generation","created":"2023-10-10","cited_by_count":0,"area":null},{"id":"2409.00322","title":"Differentially Private Synthetic High-dimensional Tabular Stream","created":"2024-08-31","cited_by_count":0,"area":null},{"id":"2410.22519","title":"Evaluating utility in synthetic banking microdata applications","created":"2024-10-29","cited_by_count":0,"area":null},{"id":"2412.03766","title":"End to End Collaborative Synthetic Data Generation","created":"2024-12-04","cited_by_count":0,"area":null},{"id":"2412.10130","title":"Optimal Bounds for Private Minimum Spanning Trees via Input Perturbation","created":"2024-12-13","cited_by_count":0,"area":null},{"id":"2506.11687","title":"Differential Privacy in Machine Learning: A Survey from Symbolic AI to LLMs","created":"2025-06-13","cited_by_count":0,"area":null},{"id":"2507.18561","title":"Beyond Internal Data: Constructing Complete Datasets for Fairness Testing","created":"2025-07-24","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":"2512.08869","title":"Differentially Private Synthetic Data Generation Using Context-Aware GANs","created":"2025-12-09","cited_by_count":0,"area":null},{"id":"2602.03791","title":"Should I use Synthetic Data for That? An Analysis of the Suitability of Synthetic Data for Data Sharing and Augmentation","created":"2026-02-03","cited_by_count":0,"area":null},{"id":"2602.04895","title":"Privacy Amplification Persists under Unlimited Synthetic Data Release","created":"2026-02-03","cited_by_count":0,"area":null},{"id":"2602.10228","title":"PRISM: Differentially Private Synthetic Data with Structure-Aware Budget Allocation for Prediction","created":"2026-02-10","cited_by_count":0,"area":null},{"id":"2603.02010","title":"Noise-Calibrated Inference from Differentially Private Sufficient Statistics in Exponential Families","created":"2026-03-02","cited_by_count":0,"area":null},{"id":"2603.27254","title":"Amalgam: Hybrid LLM-PGM Synthesis Algorithm for Accuracy and Realism","created":"2026-03-28","cited_by_count":0,"area":null},{"id":"2604.14495","title":"Decoupling Identity from Utility: Privacy-by-Design Frameworks for Financial Ecosystems","created":"2026-04-16","cited_by_count":0,"area":null},{"id":"2604.15461","title":"Evaluating LLM Simulators as Differentially Private Data Generators","created":"2026-04-16","cited_by_count":0,"area":null},{"id":"2606.02101","title":"It does what it says on the tin: safe synthetic data from coarsened margins","created":"2026-06-01","cited_by_count":0,"area":null},{"id":"2606.08372","title":"SoK: Reconstruction Attacks on Synthetic Tabular Data (Insights from Winning the NIST CRC)","created":"2026-06-06","cited_by_count":0,"area":null},{"id":"2606.11283","title":"Fixed-Parameter Tractability of Private Synthetic Data Generation","created":"2026-06-09","cited_by_count":0,"area":null},{"id":"2606.13105","title":"Disparate Impact in Synthetic Data Generation","created":"2026-06-11","cited_by_count":0,"area":null},{"id":"2607.08122","title":"Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration","created":"2026-07-09","cited_by_count":0,"area":null},{"id":"2609.03180","title":"Portable Causal Fairness Across Synthetic Data Generator Families","created":"2026-09-02","cited_by_count":0,"area":null},{"id":"2609.33363","title":"Geometry-Adaptive Mechanisms for Private Synthetic Data","created":"2026-09-27","cited_by_count":0,"area":null},{"id":"2609.39630","title":"PEG-Tab: Sampling-Time Record Repair and Release Control for Tabular Synthesis","created":"2026-09-30","cited_by_count":0,"area":null}],"next_cursor":null}