Private Synthetic Data Generation

Private synthetic data generation aims to create realistic, privacy-preserving substitutes for sensitive datasets, enabling data sharing while mitigating privacy risks. Current research focuses on improving the accuracy and realism of synthetic data using various techniques, including differentially private generative models (e.g., variational autoencoders, generative adversarial networks), knowledge graph integration to enhance data realism, and large language models for improved data generation and privacy protection. This field is crucial for advancing data-driven research and applications in sensitive domains like healthcare and finance, where responsible data sharing is paramount.

Papers

November 4, 2024

Tabular Data Synthesis with Differential Privacy: A Survey
Mengmeng Yang, Chi-Hung Chi, Kwok-Yan Lam, Jie Feng, Taolin Guo, Wei Ni
Timely Survey Differential Privacy Data Sharing Tabular Data Synthesis Private Synthetic Data Generation Differential Privacy Constraint

October 10, 2024

Evaluating Differentially Private Synthetic Data Generation in High-Stakes Domains
Krithika Ramesh, Nupoor Gandhi, Pulkit Madaan, Lisa Bauer, Charith Peris, Anjalie Field
Language Model Synthetic Data Synthetic Data Generation Private Data Private Synthetic Data Generation High Risk Domain

September 27, 2024

Differentially Private Non Parametric Copulas: Generating synthetic data with non parametric copulas under privacy guarantees
Pablo A. Osorio-Marulanda, John Esteban Castro Ramirez, Mikel Hernández Jiménez, Nicolas Moreno Reyes, Gorka Epelde Unanue
Synthetic Data Differential Privacy Synthetic Data Generation Privacy Guarantee Dimensional Copula Private Synthetic Data Generation Private Linear

September 25, 2024

KIPPS: Knowledge infusion in Privacy Preserving Synthetic Data Generation
Anantaa Kotal, Anupam Joshi
Synthetic Data Synthetic Data Generation Generative Deep Learning Private Synthetic Data Generation Knowledge Infusion User Configurable Privacy Defense

August 31, 2024

Differentially Private Synthetic High-dimensional Tabular Stream
Girish Kumar, Thomas Strohmer, Roman Vershynin
Differential Privacy Synthetic Data Generation Private Data Private Synthetic Data Generation

August 6, 2024

HARMONIC: Harnessing LLMs for Tabular Data Synthesis and Privacy Protection
Yuxin Wang, Duanyu Feng, Yongfu Dai, Zhengyu Chen, Jimin Huang, Sophia Ananiadou, Qianqian Xie, Hao Wang
Medical LLM Tabular Data Synthetic Data Generation Tabular Data Synthesis Harmonic Sound Private Synthetic Data Generation

June 3, 2024

Differentially Private Tabular Data Synthesis using Large Language Models
Toan V. Tran, Li Xiong
Differential Privacy Tabular Data Private Synthetic Data Generation Private Tabular Data

February 12, 2024

Online Differentially Private Synthetic Data Generation
Yiyun He, Roman Vershynin, Yizhe Zhu
Synthetic Data Polynomial Time Online Algorithm Online Service Private Synthetic Data Generation Optimal Accuracy

October 30, 2023

Decentralised, Scalable and Privacy-Preserving Synthetic Data Generation
Vishal Ramesh, Rui Zhao, Naman Goel
Synthetic Data Synthetic Data Generation Decentralized FL Private Synthetic Data Generation Synthetic Data Generation Pipeline

October 10, 2023

Partition-based differentially private synthetic data generation
Meifan Zhang, Dihang Deng, Lihua Yin
Synthetic Data Privacy Budget Private Synthetic Data Generation Generative Paradigm

July 4, 2023

Approximate, Adapt, Anonymize (3A): a Framework for Privacy Preserving Training Data Release for Machine Learning
Tamas Madl, Weijie Xu, Olivia Choudhury, Matthew Howard
Machine Learning Synthetic Data New Framework Differential Privacy Average Approximation Consistent Anonymization Effect Private Synthetic Data Generation Data Release Training Data Privacy

June 19, 2023

Differentially Private Synthetic Data Using KD-Trees
Eleonora Kreačić, Navid Nouri, Vamsi K. Potluru, Tucker Balch, Manuela Veloso
Synthetic Data Synthetic Data Generation Synthetic Dataset Private Synthetic Data Generation Utility Privacy Trade

June 5, 2023

Generating Private Synthetic Data with Genetic Algorithms
Terrance Liu, Jingwu Tang, Giuseppe Vietri, Zhiwei Steven Wu
Synthetic Data Genetic Algorithm Statistical Query Private Synthetic Data Generation First Order Optimization Discrete Domain

May 24, 2023

Post-processing Private Synthetic Data for Improving Utility on Selected Measures
Hao Wang, Shivchander Sudalairaj, John Henning, Kristjan Greenewald, Akash Srivastava
Synthetic Data Synthetic Data Generation Task Utility Privacy Guarantee New Measure Private Synthetic Data Generation

April 22, 2023

Differentially Private Synthetic Data Generation via Lipschitz-Regularised Variational Autoencoders
Benedikt Groß, Gerhard Wunder
Generative Model Synthetic Data Differential Privacy Private Model Private Synthetic Data Generation

December 20, 2022

Privacy-Preserving Domain Adaptation of Semantic Parsers
Fatemehsadat Mireshghallah, Yu Su, Tatsunori Hashimoto, Jason Eisner, Richard Shin
Domain Adaptation Dialogue System Semantic Parsing Semantic Parser User Utterance Private Synthetic Data Generation Semantic Coverage

November 28, 2022

On the Utility Recovery Incapability of Neural Net-based Differential Private Tabular Training Data Synthesizer under Privacy Deregulation
Yucong Liu, Chi-Hua Wang, Guang Cheng
Neural Topic Privacy Utility Privacy Analysis Privacy Constraint Private Synthetic Data Generation Private Generative Model

October 13, 2022

Secure Multiparty Computation for Synthetic Data Generation from Distributed Data
Mayana Pereira, Sikha Pentyala, Anderson Nascimento, Rafael T. de Sousa, Martine De Cock
Synthetic Data Synthetic Data Generation Data Distribution Private Synthetic Data Generation Secure Multiparty Computation Multi Party Computation

July 12, 2022

dpart: Differentially Private Autoregressive Tabular, a General Framework for Synthetic Data Generation
Sofiane Mahiou, Kai Xu, Georgi Ganev
Synthetic Data Generation Autoregressive Model General Framework Conditional Distribution Private Synthetic Data Generation

June 13, 2022

Private Synthetic Data with Hierarchical Structure
Terrance Liu, Zhiwei Steven Wu
Synthetic Data Hierarchical Structure Private Synthetic Data Generation Hierarchical Datasets