Data Synthesis based on Generative Adversarial Networks

Abstract

Privacy is an important concern for our society where sharing data with partners or releasing data to the public is a frequent occurrence. Some of the techniques that are being used to achieve privacy are to remove identifiers, alter quasi-identifiers, and perturb values. Unfortunately, these approaches suffer from two limitations. First, it has been shown that private information can still be leaked if attackers possess some background knowledge or other information sources. Second, they do not take into account the adverse impact these methods will have on the utility of the released data. In this paper, we propose a method that meets both requirements. Our method, called table-GAN, uses generative adversarial networks (GANs) to synthesize fake tables that are statistically similar to the original table yet do not incur information leakage. We show that the machine learning models trained using our synthetic tables exhibit performance that is similar to that of models trained using the original table for unknown testing cases. We call this property model compatibility. We believe that anonymization/perturbation/synthesis methods without model compatibility are of little value. We used four real-world datasets from four different domains for our experiments and conducted in-depth comparisons with state-of-the-art anonymization, perturbation, and generation techniques. Throughout our experiments, only our method consistently shows a balance between privacy level and model compatibility.

Full Version - VLDB 2018

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Cited by in corpus (52)

• AutoML: A Survey of the State-of-the-Art
• Deep Neural Networks and Tabular Data: A Survey
• Data Synthesis based on Generative Adversarial Networks
• A Survey on Data Collection for Machine Learning: a Big Data -- AI Integration Perspective
• Automated data processing and feature engineering for deep learning and big data applications: a survey
• Differentially Private Synthetic Medical Data Generation using Convolutional GANs
• When Machine Learning Meets Privacy: A Survey and Outlook
• Modeling Tabular data using Conditional GAN
• CTAB-GAN: Effective Table Data Synthesizing
• Evaluating the Clinical Realism of Synthetic Chest X-Rays Generated Using Progressively Growing GANs
• Copula Flows for Synthetic Data Generation
• Model-Based Counterfactual Synthesizer for Interpretation
• Fed-TGAN: Federated Learning Framework for Synthesizing Tabular Data
• Differentially Private Synthetic Data: Applied Evaluations and Enhancements
• Controllable Data Generation by Deep Learning: A Review
• A Consensus Privacy Metrics Framework for Synthetic Data
• DP-MERF: Differentially Private Mean Embeddings with Random Features for Practical Privacy-Preserving Data Generation
• Generative Adversarial Networks: A Survey Towards Private and Secure Applications
• Balancing thermal comfort datasets: We GAN, but should we?
• CasTGAN: Cascaded Generative Adversarial Network for Realistic Tabular Data Synthesis
• Electra: Conditional Generative Model based Predicate-Aware Query Approximation
• Approximate Query Processing using Deep Generative Models
• GAN-based Tabular Data Generator for Constructing Synopsis in Approximate Query Processing: Challenges and Solutions
• Transfer Learning for Thermal Comfort Prediction in Multiple Cities
• Relational Data Synthesis using Generative Adversarial Networks: A Design Space Exploration
• MaaSDB: Spatial Databases in the Era of Large Language Models (Vision Paper)
• DTGAN: Differential Private Training for Tabular GANs
• Generative Synthesis of Insurance Datasets
• KiNETGAN: Enabling Distributed Network Intrusion Detection through Knowledge-Infused Synthetic Data Generation
• Causal-TGAN: Generating Tabular Data Using Causal Generative Adversarial Networks
• Differentially Private GANs for Generating Synthetic Indoor Location Data
• Systematic Assessment of Tabular Data Synthesis
• Artificial Inductive Bias for Synthetic Tabular Data Generation in Data-Scarce Scenarios
• Differentially Private Synthetic Data Generation Using Context-Aware GANs
• Neural Click Models for Recommender Systems
• GAN Memory with No Forgetting
• Holdout-Based Fidelity and Privacy Assessment of Mixed-Type Synthetic Data
• Effective and Privacy preserving Tabular Data Synthesizing
• Synthetic Flight Data Generation Using Generative Models
• Synthesising Multi-Modal Minority Samples for Tabular Data
• TableGAN-MCA: Evaluating Membership Collisions of GAN-Synthesized Tabular Data Releasing
• A Differentially Private Multi-Output Deep Generative Networks Approach For Activity Diary Synthesis
• SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework
• Composable Generative Models
• Machine Learning in Epidemiology
• Preservation of Anomalous Subgroups On Machine Learning Transformed Data
• Towards Generating Real-World Time Series Data
• Adversarial Machine Learning-Enabled Anonymization of OpenWiFi Data
• ORGAN: Object-Centric Representation Learning using Cycle Consistent Generative Adversarial Networks
• STAN: Synthetic Network Traffic Generation with Generative Neural Models
• Improving Model Compatibility of Generative Adversarial Networks by Boundary Calibration
• Generative Augmentation of Imbalanced Flight Records for Flight Diversion Prediction: A Multi-objective Optimisation Framework